Industrial Object Dispatching System Architectures

Industrial Dispatch Systems Architecture: Multi-tier Approach with MQTT and PostgreSQL

Industrial dispatch systems for industrial objects typically use a multi-tier architecture with event-driven data transmission via MQTT, central storage based on PostgreSQL, and visualization through specialized platforms. For industrial objects with Siemens PLC equipment, the typical architecture includes data collection (PLC → MQTT broker), processing (Python microservices), storage (PostgreSQL), and visualization (Visiology) layers, with UML diagrams for design and data security.

Contents

• Basic Architectural Patterns
• Layered Architecture of Dispatch Systems
• UML Diagrams for Design
• Security and Data Privacy
• Technology Stack and Integration
• Compliance with Russian Software Requirements
• Practical Implementation and Expansion

Basic Architectural Patterns

Dispatch systems for industrial objects are implemented using several standard architectural approaches. The most common is the Multi-tier Architecture, which ensures separation of responsibilities between system components.

For industrial objects with Siemens PLC equipment, an Event-Driven Architecture is often applied, where data from industrial controllers is transmitted via the MQTT protocol. According to research, this architecture enables real-time data processing and scalability [source].

Another important pattern is the Microservices Architecture, which divides system functionality into independent services:

• Data Collection Service
• Data Processing Service
• Analytics Service
• Visualization Service

This architecture aligns with modern industrial automation practices and allows each system component to be scaled independently.

Layered Architecture of Dispatch Systems

A typical dispatch system for industrial objects has a five-tier architecture:

Data Acquisition Layer

At this level, Siemens PLC equipment (e.g., SIMATIC S7-300) connects to the system through industrial networks. Controllers transmit data about equipment status, process parameters, and emergency situations.

Data Transmission Layer

An MQTT broker is used for reliable data transmission from controllers to the processing system. MQTT provides:

• Publish/subscribe for efficient data distribution
• QoS (Quality of Service) levels for guaranteed delivery
• TLS/SSL encryption for secure transmission

Data Processing Layer

Python microservices perform the following functions:

• Data aggregation from 30 objects
• Anomaly and deviation processing
• Calculation of production indicators
• Data preparation for visualization

Data Storage Layer

PostgreSQL serves as the central data repository with a relational structure optimized for time series and analytical queries.

Visualization Layer

Visiology provides interactive data visualization with dashboards, reports, and real-time monitoring.

UML Diagrams for Design

Various types of UML diagrams are used to design industrial dispatch systems:

Component Diagram

The component diagram shows the system structure at the module level. For an industrial dispatch system, it includes:

[Siemens PLC] → [MQTT Broker] → [Python Data Processor] → [PostgreSQL] → [Visiology]

Deployment Diagram

The deployment diagram describes the physical distribution of system components across infrastructure:

• Data Collection: Terminals at objects with PLC connections
• Central Server: MQTT broker, Python services, PostgreSQL database
• Visualization: Visiology server with web interface

Sequence Diagram

Shows component interaction during data transmission from PLC to visualization:

sequenceDiagram
    participant PLC as Siemens PLC
    participant MQTT as MQTT Broker
    participant Python as Python Service
    participant DB as PostgreSQL
    participant Vis as Visiology
    
    PLC->>MQTT: Publish data (topic: object_1/sensors)
    MQTT->>Python: Subscription and message delivery
    Python->>DB: Data storage
    Python->>Vis: Dashboard preparation
    Vis->>User: Data visualization

Security and Data Privacy

For dispatch systems with enhanced security requirements, the following measures are implemented:

Authentication and Authorization

• RBAC (Role-Based Access Control) for access control
• Two-factor authentication for administrators
• Client-side data encryption

Protection of Transmitted Data

• TLS 1.3 for MQTT connections
• Field-level encryption in PostgreSQL
• Input data validation at all levels

Compliance with FZ-152 Requirements

• Personal data anonymization
• Row-level security
• Audit of all data operations

Industrial Equipment Protection

• Isolation of industrial networks from corporate networks
• Regular PLC firmware updates
• Monitoring of unauthorized access

Technology Stack and Integration

MQTT Broker

For industrial dispatch systems, the following is recommended:

• EMQX or HiveMQ as the broker
• Configuration with high fault tolerance
• Scalability for 30+ simultaneous connections

PostgreSQL Database

Optimization for industrial systems:

• TimescaleDB extension for time series
• Partitioning tables by objects and time
• Indexes for fast analytics

Python Services

Microservices structure:

# Example of microservice structure
class DataProcessor:
    def __init__(self):
        self.mqtt_client = mqtt.Client()
        self.db_connection = psycopg2.connect()
    
    def process_sensor_data(self, topic, payload):
        # Data validation
        # Deviation calculation
        # Database storage
        pass

Visiology Integration

API integration for automatic dashboard updates based on new data.

Compliance with Russian Software Requirements

To comply with the Russian software registry requirements:

Licensing

• Use of open-source components with Russian documentation
• Confirmation of absence of foreign dependencies

Legal Aspects

• Hosting on Russian territory
• Data processing agreements
• Documentation in Russian

Technical Requirements

• Support for GOST R standards
• Cryptographic algorithms according to FSB standards
• Interface localization

Certification Process

• Preparation of document package
• Testing in accredited laboratories
• Training of technical support

Practical Implementation and Expansion

System Implementation Stages

1. Architecture Design

• UML diagram development
• Infrastructure selection
• Security planning

2. Core System Implementation

• MQTT broker configuration
• PostgreSQL schema creation
• Python services development

3. Equipment Integration

• Siemens PLC communication setup
• Data collection testing
• Sensor calibration

4. Visualization and Analytics

• Dashboard creation in Visiology
• Deviation algorithm development
• Report configuration

Functionality Expansion Plan

Logistics Control

• Equipment movement tracking module
• Delivery time analytics
• Route optimization

Maintenance Statistics

• Planned maintenance
• Failure analysis
• Component replacement forecasting

Advanced Reporting

• Export to various formats
• Automatic document generation
• ERP system integration

Sources

1. Structure of automation system Siemens S7-300
2. The PLC UML State-chart design pattern
3. MQTT Data Integration with PostgreSQL
4. MQTT Data Visualization with Grafana
5. EMQX PostgreSQL Integration
6. Unified Modeling Language (UML) Diagrams
7. Industrial IoT Reference Architecture

Conclusion

To implement a dispatch system for 30 industrial objects with Siemens PLC equipment, a multi-tier architecture with MQTT protocol for data transmission, PostgreSQL for storage, and Visiology for visualization is recommended. UML component and deployment diagrams will help design the system with security requirements and Russian software compliance in mind.

Practical steps include:

1. Develop an architecture with clear layer separation
2. Implement secure data transmission via MQTT
3. Configure PostgreSQL for efficient time series storage
4. Create Python microservices for anomaly processing
5. Integrate Visiology for interactive visualization
6. Ensure compliance with FZ-152 and Russian software registry requirements

Further system expansion in logistics, maintenance, and reporting areas is possible by adding specialized modules based on the existing infrastructure.