- [Public] Big Data-Based Water Quality Measurement and Monitoring Service
- Project Name
- Big Data-Based Water Quality Measurement and Monitoring Service
- PaaS-TA Coverage
- Platform
- Company/Institution Name
- K-water sewage treatment facilities
- Outline
-
- Improve monitoring services to the cloud environment that can measure and check water quality values in real-time after installing sensors in sewage treatment facilities
- Promotion Background
-
- Download and install the apk file directly on the user's phone for pre-developed monitoring services
- Identify the need for deployment development to deploy and deliver services through a cloud environment
- Deployment Content
-
- Build of Cloud Platform
- Utilize and validate cloud platform infrastructure for cloud transformation
- Ternant configuration and NKS for each sewage treatment facility to implement management functions such as securing container base and rapid elasticity such as Auto Scaling
- Completed configuration diagram and architecture for service
- Established data verification and task management systems
- Visualized data for user convenience (provides color step by step, graphs of water quality value data)
- Universal network access available (available on PC and mobile etc.)
- Build of Cloud Platform
- Characteristics of the Configuration
-
- Provides and manages systematic and reliable information
- Utilizes cloud platform information protection infrastructure to provide services in a secure cloud environment
- Checked and reflected virtualization security and access control, network security, etc. configured in public zones
- Strengthen efficiency by establishing a digital work system for administrators
- Improved efficiency and established a quick decision-making and response system by checking and managing real-time measured water quality data
- Documented functional definitions through user and administrator manuals to create a working system and provide process flexibility
- Provides user-centered information for fast decision-making and reduces work processing time
- Strengthen the ability to respond to the 4th industrial revolution by introducing future technologies through Cloud, artificial intelligence (AI), etc.
- Provides and manages systematic and reliable information
- Image #1
- Feature Extraction → Data Processing
- Development of real-time measurement technology → Store Data → Correlation analysis of measurement factors (turbidity, residual chlorine, PH, temperature, conductivity,BOD,COD,SS,T-N,T-P) →
- → Creating a Statistical Code Model → Development of data learning and analysis predictions → Store Data
- → Creating a Statistical Code Model → Development of information provision and feedback systems
- 1.Develops real-time measurement technology
- Alternative items that can withstand high concentrations of incoming sewage can be measured in real time
- 2.Develops data learning and analytics predictions
- Provides predictive values by analyzing big data and developing correlation algorithms for water quality factors
- 3.Develops information provision and feedback systems
- Provides real-time measurement and analysis technologies and providing contamination alerts and feedback
- Feature Extraction → Data Processing
- Image #2
- Users : Admin, User
- NCP
- Source Commit
- Application Code
- Kubernetes yaml Template
- Source Pipeline
- Source Deploy
- Source Build
- Container Registry
- Anti-DDoS
- IDOS
- IPS
- WAF
- VCP
- Private Subnet
- Server access control
- Private Subnet
- Master Node
- Public Subnet
- Worker Node
- Portal
- POD
- Portal
- Worker Node
- Monitoring facility data
- POD
- Monitoring facility data
- Worker Node
- Private Subnet(Active DB)
- Worker Node
- User, Manages facility information
- POD
- User, Manages facility information
- Worker Node
- facility Sensor Data Collect
- POD
- facility Sensor Data Collect
- Maria DB(PostgreSQL)
- Worker Node
- Private Subnet(StandBy DB)
- Worker Node
- User, Manages facility information
- POD
- User, Manages facility information
- Worker Node
- facility Sensor Data Collect
- POD
- facility Sensor Data Collect
- Maria DB(PostgreSQL)
- Worker Node
-
- Log backup
- Log Data
- Storage
- Private Subnet
- Source Commit
- IoT Sensor
- SSL
- Google Firebase DB
- Admin →
- (NCP)
- → Source Commit → Source Pipeline/Source Deploy/ Source Build → Container Registry →
- (VPC)
- → Public Subnet
- (VPC)
- → ssl Vpn
- (VPC)
- → Server access control of Private Subnet → Private Subnet for Master Node →
- → Public Subnet
- → Connecting a private subnet (StandBy DB) with Sensor Data Collect in a worker node in a private subnet (Active DB) → Google Firebase DB outside VPC and NCP
- → Source Commit → Source Pipeline/Source Deploy/ Source Build → Container Registry →
- (NCP)
- User →
- (NCP)
- → Anti-DDoS/IDOS/IPS/WAF→
- (VPC)
- → Public Subnet
- (VPC)
- → Anti-DDoS/IDOS/IPS/WAF→
- (NCP)
- IoT Sensor → SSL → Google Firebase DB