Big Data Analysis Model of Customer Appeal Based on Power Enterprise Service Platform

2020 ◽  
Author(s):  
Zhenhua Liu ◽  
Liwei Su
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Analysis Model ◽  
Service Platform ◽  
Power Enterprise

scholarly journals A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

2019 ◽  
Vol 11 (13) ◽  
pp. 3499 ◽  
Author(s):  
Se-Hoon Jung ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Transmission Line ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Principal Component ◽  
Big Data Analysis ◽  
Standard Normal Distribution ◽  
Analysis Model ◽  
Q Learning

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

scholarly journals Application of the Big Data Analysis Model in Higher Education Talent Training Quality Evaluation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lijun Li ◽  
Wentao Wang ◽  
Fei Bian
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analysis ◽  
Evaluation System ◽  
Big Data Analysis ◽  
Analysis Model ◽  
Weight Relationship ◽  
Training Quality ◽  
Relationship Of ◽  
Educational Indicators

With the increasing demand for applied and professional talents, the talent market has been in short supply. Although there are many talents in the talent market, the quality of talents cannot keep up with the development of quantity. Therefore, it is of great practical significance to establish a visual evaluation system of personnel training quality in the field of higher education. In view of the unreasonable evaluation and unclear weight relationship in the evaluation of educational indicators, this paper puts forward a big data analysis model to comprehensively evaluate teaching evaluation indicators, which has more scientific significance. In this paper, different systems in the index system are used as the analysis objects and the first-level weight relationship is normalized, which can distribute the weights more reasonably. Through the big data analysis method, the teaching quality evaluation system is more reasonable and scientific. In this paper, the quality index system for higher education background is designed and constructed and the weight relationship of different educational indicators is analyzed through big data, and four main indicators are obtained; then, the weight relationship of secondary indicators is analyzed, and finally, the weight relationship of all indicators is formed. The results show that the weight relationship of four indexes is 0.3285, 0.1973, 0.2967, and 0.1755, and the evaluation model of education quality is given.

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