A New Approach of Expanding Data Processing Ability for Configuration Monitoring Software MCGS Based on OLE

2011 ◽  
Vol 65 ◽  
pp. 295-298 ◽  
Author(s):  
Fan Yang ◽  
Cai Li Zhang
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Monitoring System ◽  
Programming Language ◽  
Configuration Software ◽  
Time Data ◽  
Software Configuration ◽  
Processing Ability ◽  
Significant Performance ◽  
Monitoring Software

Considering the insufficient ability of data processing existed in configuration software, a scheme integrated both advantages of advanced programming language and configuration software is provided. In this scheme real-time data acquisition and complex processing are achieved by advanced programming language, the human-computer interface and other functions of the monitoring system are achieved by configuration software. Configuration software achieves the purpose of expanding data processing ability by data communications between advanced programming language and configuration software based on OLE technology. The practical application result indicates that the data processing ability of configuration software can be effectively expanded based on OLE technology, which has well stability and real-time, and can play significant performance in complex parameters and data processing related monitoring system.

scholarly journals A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2183 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Muhammad Ijaz ◽  
M. Syaekhoni ◽  
Norma Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Monitoring System ◽  
Diabetic Patients ◽  
Time Data ◽  
Personalized Healthcare ◽  
Real Time Data ◽  
Healthcare Monitoring ◽  
Real Time Data Processing

Zigbee Technology in the Application of Remote Monitoring System of Tubular Furnace

2013 ◽  
Vol 753-755 ◽  
pp. 2252-2255
Author(s):  
Zai Ping Chen ◽  
Feng Liu ◽  
Quan Sheng Sun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Configuration Software ◽  
Wireless Transmission ◽  
Time Data ◽  
Remote Monitoring System ◽  
Zigbee Technology ◽  
Wireless Environment ◽  
Petrochemical Enterprise

In view of the problem about off-line monitoring lagging and the high cost of cable transfer with present traditional petrochemical enterprise, this article provides remote monitoring system design and implementation of the heating furnace based on the Zigbee wireless transmission technology. Specifically, configuration software can realized real-time monitoring of furnace combustion condition by sending detected smoke real-time data which goes through flue gas analyzer to Siemens PLC, and transferring it to the industrial computer by Zigbee wireless transmission technology. Simulation result shows that the system has well real-time performance and low power consumption advantages which can complete effective monitoring in the wireless environment of furnace combustion state.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

A Study of IoT based Real-Time Solar Power Remote Monitoring System

2021 ◽  
Vol 9 (2) ◽  
pp. 27-36
Author(s):  
Sheikh Hasib Cheragee ◽  
Nazmul Hassan ◽  
Sakil Ahammed ◽  
Abu Zafor Md. Touhidul Islam
Keyword(s):  
Power Plant ◽  
Real Time ◽  
Monitoring System ◽  
Power Output ◽  
Solar Power ◽  
Solar Power Plant ◽  
Solar Pv ◽  
Time Data ◽  
Power Monitoring ◽  
Remote Monitoring System

We have Developed an IoT-based real-time solar power monitoring system in this paper. It seeks an opensource IoT solution that can collect real-time data and continuously monitor the power output and environmental conditions of a photovoltaic panel.The Objective of this work is to continuously monitor the status of various parameters associated with solar systems through sensors without visiting manually, saving time and ensures efficient power output from PV panels while monitoring for faulty solar panels, weather conditionsand other such issues that affect solar effectiveness.Manually, the user must use a multimeter to determine what value of measurement of the system is appropriate for appliance consumers, which is difficult for the larger System. But the Solar Energy Monitoring system is designed to make it easier for users to use the solar system.This system is comprised of a microcontroller (Node MCU), a PV panel, sensors (INA219 Current Module, Digital Temperature Sensor, LDR), a Battery Charger Module, and a battery. The data from the PV panels and other appliances are sent to the cloud (Thingspeak) via the internet using IoT technology and a Wi-Fi module (NodeMCU). It also allows users in remote areas to monitor the parameters of the solar power plant using connected devices. The user can view the current, previous, and average parameters of the solar PV system, such as voltage, current, temperature, and light intensity using a Graphical User Interface. This will facilitate fault detection and maintenance of the solar power plant easier and saves time.

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