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5.1.1Platform Construction: Demand Analysis课程教案、知识点、字幕


OK

欢迎回来
Welcome back

让我们从第五章开始
Let's start about Chapter five in Part 2

智能电网中大数据的研究方法与实践
Research methods and practice of big data in smart grid

这章共有4个小节
There are four sections in Chapter 5

5.1
5.1

平台建设
Platform construction

数据的收集和扩散
Data collection and management Data aggregation and fusion

后面的部分我们只关注第一部分
Analyses and mining And this lecture we'll just focus on the first part of the section

5.1.1
5.1.1

这是第一部分
It's the first subsection

这节课
This lecture

我们只关注5.1.1需求分析
We'll just focus on the first subsection of 5.1

智能大数据平台建设
demand analyses In the construction of intelligent big data platform

Firstly
首先

首先要综合分析业务需求
it is necessary to comprehensively analyze the business requirements

梳理业务应用对数据采集
Find out the common requirements of business applications for data collection

数据存储
data storage

数据处理计算、分析挖掘及可视化展现的共性需求
data processing and calculation analysis and mining and visual display

其次
Secondly

要明确建设目标、设定建设原则及制定建设方案
it is necessary to define the construction objectives set the construction

要明确建设目标、设定建设原则及制定建设方案
principles and formulate the construction platform

最后
Finally

进行平台开发、测试及部署
it is necessary to develop,test and deploy the platform

由此可见 平台的构建可以通过以下步骤来实现
So we can see that the platform construction can be achieved by the following steps

第一个是业务需求
The first is business requirements

第二
And the second

作为生产力的建设
as construction objectives

发展
develop

测试和租金
test and arrange

开展面向新能源
Carry out demand research for new energy

调度、高压
dispatching, high voltage

营销及继电保护等电网业务领城的需求调研
marketing relay protection and other power

营销及继电保护等电网业务领城的需求调研
grid businesses

了解这些业务在数据类型及容量
Understand the demands of this business in data type and capacity

数据存储方式和速度
Data storage mode and speed

数据采集频率及传输方式
data collection

数据采集频率及传输方式
frequency and transmission mode

数据计算模式及复杂度
Data caculation

数据计算模式及复杂度
mode and complexity

分析和挖掘算法
analyses and excavation algorithms

可视化形式等方面的需求
visualization forms and other aspects

梳理汇总调研结果
Summarize the survey results

分析并提炼出共性
analyze and extract the common

可量化、可实施的大数据需求,为平台建设方案的制定提供依据
quantifiable and implementable big data needs, and provide basis

可量化、可实施的大数据需求,为平台建设方案的制定提供依据
for the formulation of platform construction plan

所以
Therefore

通过需求调研、需求梳理汇总、需求分析转换
based on demand

通过需求调研、需求梳理汇总、需求分析转换
research sorting and a summary and demand analysis transformation

就完成平台建设中需求分析的工作
we can complete the work of demand analyses in platform construction


Ok

这节课就到这里
that's all for this lecture

下节课将平台建设的第二部分即方案设计
Next time we will talk about the second

下节课将平台建设的第二部分即方案设计
part of platform construction.

关于平台设计的详细内容
Some details of platform design

再见
Hope to see you again

祝你愉快
Have a nice day

Big Data of Smart Grid课程列表:

Chapter 1 What is Big Data

-Course Introduction and Overview of Big Data

-Chapter 1

-Big data review literature

Chapter 2 Big Data of Smart Grid(BDSG)

-2.1 Why Electirc Power + Big Data? 2.2 Applications

-Chapter 2

-An important application of big data in electric power——literature on the identification of small targets such as faults

Chapter 3 Main Application Fields of BDSG

-3.1 Grid Operation and Development

-3.2 Power Consumers

-3.3 Society and Government

-Chapter3

-Related literature on big data applications from the user perspective

Chapter 4 Technology System of BDSG

-4.1 Data Acquisition+4.2 Data Storage

-4.3 Data Processing

-4.4 Data Analysis and Mining

-4.5 Data Visualization

-4.6 Data Security and Privacy Protection

-Chapter4

-Load forecasting technology related literature

Chapter 5 Research Methods and Application Methods of BDSG

-5.1.1Platform Construction: Demand Analysis

-5.1.2Platform Construction: Design (1)

-5.1.2Platform Construction: Design (2)

-5.2 Data collection and management

-5.3.1 Data Aggregation and Fusion: Scheme and process

-5.3.2 Data Aggregation and Fusion: Application Practice

-5.4.1 Analysis and Mining: Scheme and process

-5.4.2 Analysis and Mining: Use-case analysis

-Chapter5

-Commonly used electric power big data deep learning method——application literature of transfer learning

Chapter 6 Project Cases of BDSG

-6.1 Heavy overload prediction of station area

-6.2 Daily load forecasting of large users

-6.3 Fault correlation analysis of power grid control system equipment

-6.4 Reliability of relay protection equipment family-

-6.5Application of random matrix in big data analysis of smart grid

-Chapter6

-Literature on Power Vision Data Processing Technology

Chapter 7 Prospect of BDSG

-Development trend and suggestions for BDIG

-Chapter7

-Intelligent Disaster or Failure Recognition Means——Related Literature of Electric Power Vision Big Data

5.1.1Platform Construction: Demand Analysis笔记与讨论

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