Big Data Processing Method for Multi-dimensional Power Supply Reliability of Terminal Users

2020 ◽  
Author(s):  
Shigong Jiang ◽  
Weihong Yang ◽  
He Yang ◽  
Lihu Jia ◽  
Chongyang Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Power Supply ◽  
Processing Method ◽  
Big Data Processing ◽  
Data Processing Method ◽  
Supply Reliability

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.

scholarly journals A Study on the Qualitative Data Analysis for Customer Experience Management Strategy

2021 ◽  
Vol 12 (5) ◽  
pp. 249-256
Author(s):  
Jinho Ahn, Jeungsun Lee
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Quantitative Data ◽  
Positive Emotions ◽  
Customer Experience ◽  
Processing Method ◽  
Point Of View ◽  
Experience Economy ◽  
Customer Experience Management ◽  
Data Processing Method

Securing customer experience data that creates positive emotions for customers and differentiates them from products and services from competitors is becoming important to a company's growth engine. In particular, an important factor in the management of experience data requires a qualitative-based experience data processing method to secure good experience data different from the quantitative data collection such as big data and processing method. With the emergence of the experience economy, it is very important for companies to collect and process experience data in the existing big data processing method. However, the experience data processing method based on big data that analyses the current quantitative data is difficult to provide good experience data from a corporate data strategic point of view. In particular, for corporate customer experience management, mix studies are required for analysis method of qualitative experience data to meaningfully interpret the expansive quantitative experience data of big data and phenomena and context in social science. This is because it is possible to discover the meaning of experience data by reading the context of phenomena by collecting experiences through ethnography methods such as observation or interviewing the context that could not be read in the process of processing the vast quantitative experience data of the big data method. In this study, the first processing was performed as an affinity diagram through a method of collecting experience data using ethnography method. Secondly, the effect of the qualitative experience data processing method on customer experience management, customer loyalty reinforcement, and enterprise value creation was studied. As a result, only the research hypothesis that there was a direct relationship between the affinity method and the utilization of experience data was rejected, and all the research settings set for the remaining qualitative experience data processing and utilization model were adopted.

scholarly journals A real-time parallel data acquisition and big data processing method for four-in-one optical fiber sensor network

AIP Advances ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 075019
Author(s):  
Wanshan Zhu ◽  
Junfeng Jiang ◽  
Jin Wang ◽  
Xinggang Liu ◽  
Tiegen Liu
Keyword(s):  
Big Data ◽  
Optical Fiber ◽  
Data Processing ◽  
Real Time ◽  
Sensor Network ◽  
Optical Fiber Sensor ◽  
Processing Method ◽  
Fiber Sensor ◽  
Data Processing Method ◽  
Parallel Data

A Framework for Processing Water Resources Big Data and Application

2014 ◽  
Vol 519-520 ◽  
pp. 3-8 ◽  
Author(s):  
Ping Ai ◽  
Zhao Xin Yue
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Water Resources ◽  
Data Processing ◽  
Dynamic Analysis ◽  
Processing Method ◽  
Application Technology ◽  
Data Processing Method ◽  
Processing Water ◽  
Related Application

The development of information technology expands the spatial and temporal scale and types of elements of the water resources information, making the water resources data show the characteristics of multi-source, heterogeneous, massive, and the traditional data processing method is difficult for fine processing and dynamic analysis. Combined with the "4v" characteristics of big data, we put forward a framework for processing water resources big data, to process and analyze modern water resources data for real-time and rapid, and discuss the related application. Based on the features of modern water resources data, this paper discusses the characteristics and application technology of big data, and briefly describes a framework for processing water resources big data and application.

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