Industrial AI Assistant MCP Server
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Industrial AI Assistant MCP Serveranalyze pressure trends in reactor R-101 for the last 30 minutes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Industrial AI Assistant Framework
An open-source framework for integrating LLM-based AI assistants with Industrial Control Systems (ICS/DCS) via MCP protocol and Composio, featuring domain expert knowledge embedding.
English
Overview
This project provides a reference architecture and implementation framework for building AI-powered assistant systems that can:
Monitor real-time industrial process data from DCS/SCADA systems
Apply domain expert knowledge for intelligent anomaly detection
Generate actionable recommendations for control room operators
Provide field operation guidance through mobile interfaces
Key Innovation: The framework demonstrates how to embed domain expert thinking patterns (like pressure balance analysis, causal chain reasoning) into LLM-based AI systems for industrial applications.
⚠️ Important Disclaimer
This is a reference implementation and research framework only:
✅ Suitable for: Learning, research, proof-of-concept development
✅ Contains: Generic architecture patterns, integration methodologies
❌ Does NOT contain: Production-ready code, specific device configurations, proprietary industrial data
⚠️ WARNING: Direct connection to production DCS systems requires thorough security assessment and should only be done by qualified industrial automation professionals
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ AI Assistant Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Domain Expert │ │ Knowledge │ │ Decision │ │
│ │ Persona │ │ Retrieval │ │ Engine │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ └─────────────────┼─────────────────┘ │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ MCP Server │ │
│ │ (Model Context │ │
│ │ Protocol) │ │
│ └──────────┬──────────┘ │
└───────────────────────────┼─────────────────────────────────────┘
│
┌───────────────────────────┼─────────────────────────────────────┐
│ Integration Layer (Composio) │
│ ▼ │
│ ┌─────────────────────┐ │
│ │ Protocol Adapters │ │
│ │ (OPC UA/Modbus/API)│ │
│ └──────────┬──────────┘ │
└───────────────────────────┼─────────────────────────────────────┘
│
┌───────────────────────────┼─────────────────────────────────────┐
│ Industrial Control System (Read-Only) │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ DCS/SCADA/PLC Systems │ │
│ │ (Via OPC UA/etc) │ │
│ └─────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘Core Concepts
1. Domain Expert Knowledge Embedding
The framework demonstrates how to structure and embed industrial domain knowledge:
# Example: Expert reasoning pattern
class ExpertAnalyzer:
"""
Implements expert thinking patterns for industrial process analysis:
- Pressure balance analysis
- Causal chain derivation
- Design intent understanding
"""
def analyze_pressure_system(self, upstream_pressure, downstream_pressure):
"""
Expert heuristic: All fluid systems are pressure balance problems
"""
pressure_diff = upstream_pressure - downstream_pressure
if pressure_diff < 0:
return {
"issue": "Pressure mismatch",
"reason": "Low pressure cannot feed high pressure system",
"recommendation": "Check pump status or upstream blockages"
}
return {"status": "normal", "pressure_diff": pressure_diff}2. Safety-First Architecture
Security Zones:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Level 3 │ ←→ │ Level 2 │ ←→ │ Level 1 │
│ (AI Layer) │ │ (Data Layer)│ │ (DCS Layer) │
│ │ │ │ │ READ-ONLY │
└─────────────┘ └─────────────┘ └─────────────┘
↑ ↑
Unidirectional Control Network
Data Diode (Isolated)Key Safety Principles:
✅ Read-only by default - AI never writes to DCS directly
✅ Human-in-the-loop - All recommendations require operator confirmation
✅ Fail-safe design - AI failure does not affect DCS operation
✅ Physical isolation - Network segmentation with data diodes
Project Structure
industrial-ai-assistant/
├── docs/
│ ├── architecture.md # System architecture documentation
│ ├── security.md # Security guidelines
│ ├── mcp-integration.md # MCP protocol integration guide
│ └── expert-knowledge.md # How to embed domain expertise
├── src/
│ ├── mcp_server/ # MCP server implementation
│ │ ├── server.py
│ │ └── tools/
│ ├── knowledge_base/ # Knowledge base structures
│ │ ├── equipment.py
│ │ ├── process.py
│ │ └── expert_patterns.py
│ ├── analyzers/ # Analysis engines
│ │ ├── pressure_analyzer.py
│ │ ├── trend_analyzer.py
│ │ └── causal_analyzer.py
│ └── interfaces/ # User interfaces
│ ├── control_room.py
│ └── field_assistant.py
├── examples/
│ ├── chemical_process/ # Chemical industry example
│ ├── power_plant/ # Power generation example
│ └── oil_gas/ # Oil & gas example
├── tests/
└── README.mdQuick Start
Prerequisites
Python 3.9+
Node.js 18+ (for MCP components)Installation
# Clone the repository
git clone https://github.com/yourusername/industrial-ai-assistant.git
cd industrial-ai-assistant
# Install dependencies
pip install -r requirements.txt
# Install MCP SDK
npm install @modelcontextprotocol/sdkBasic Usage
from industrial_ai import MCPClient, ExpertAnalyzer
# Initialize MCP client
client = MCPClient(
server_url="opc.tcp://your-dcs-server:4840",
read_only=True # Safety: enforce read-only mode
)
# Connect to knowledge base
knowledge_base = ExpertAnalyzer.load_knowledge_base("examples/chemical_process")
# Analyze real-time data
while True:
data = client.read_process_data(tags=["TEMP_001", "PRESS_001", "FLOW_001"])
# Apply expert analysis
result = knowledge_base.analyze(data)
if result.has_anomaly:
print(f"⚠️ Anomaly detected: {result.description}")
print(f"💡 Recommendation: {result.recommendation}")
time.sleep(5)Integration Examples
OPC UA Integration
from industrial_ai.adapters import OPCUAAdapter
adapter = OPCUAAdapter(
endpoint="opc.tcp://dcs-server:4840",
security_policy="Basic256Sha256"
)
# Read process variables
values = adapter.read_values([
"ns=2;s=Reactor.Temperature",
"ns=2;s=Reactor.Pressure"
])MCP Server Configuration
{
"mcpServers": {
"industrial-dcs": {
"command": "python",
"args": ["-m", "industrial_ai.mcp_server"],
"env": {
"DCS_ENDPOINT": "opc.tcp://localhost:4840",
"READ_ONLY_MODE": "true"
}
}
}
}Knowledge Base Template
Create your own domain knowledge base:
# knowledge_base/equipment_template.yaml
equipment:
- id: REACTOR_001
name: Gasification Reactor
type: reactor
parameters:
- name: temperature
tag: TI_001
normal_range: [1400, 1700]
unit: celsius
alarms:
high: 1750
high_high: 1800
- name: pressure
tag: PI_001
normal_range: [4.5, 5.5]
unit: mpa
expert_rules:
- name: pressure_balance_check
description: "Verify pressure differential across reactor"
condition: "inlet_pressure - outlet_pressure < 0.1"
action: "alert_operator"
- name: temperature_trend
description: "Monitor temperature change rate"
condition: "temperature_slope > 50"
action: "predictive_alert"Related MCP server: @inscada/mcp-server
中文
项目概述
本项目提供了一个参考架构和实现框架，用于构建能够执行以下功能的AI辅助工业控制系统：
从DCS/SCADA系统监控实时工业过程数据
应用领域专家知识进行智能异常检测
为控制室操作员生成可执行的建议
通过移动界面提供现场操作指导
核心创新：该框架展示了如何将领域专家思维模式（如压力平衡分析、因果链推导）嵌入到基于LLM的工业AI系统中。
⚠️ 重要声明
这只是一个参考实现和研究框架：
✅ 适用于：学习、研究、概念验证开发
✅ 包含：通用架构模式、集成方法论
❌ 不包含：生产就绪代码、特定设备配置、专有工业数据
⚠️ 警告：直接连接生产DCS系统需要全面的安全评估，且只能由合格的工业自动化专业人员执行
核心概念
1. 领域专家知识嵌入
框架展示了如何结构化和嵌入工业领域知识：
# 示例：专家推理模式
class ExpertAnalyzer:
"""
实现工业过程分析的专家思维模式：
- 压力平衡分析
- 因果链推导
- 设计意图理解
"""
def analyze_pressure_system(self, upstream_pressure, downstream_pressure):
"""
专家启发式：所有流体系统都是压力平衡问题
"""
pressure_diff = upstream_pressure - downstream_pressure
if pressure_diff < 0:
return {
"issue": "压力不匹配",
"reason": "低压无法向高压系统输送",
"recommendation": "检查泵状态或上游堵塞"
}
return {"status": "正常", "pressure_diff": pressure_diff}2. 安全第一架构
关键安全原则：
✅ 默认只读 - AI绝不直接向DCS写入
✅ 人机协同 - 所有建议需要操作员确认
✅ 故障安全设计 - AI故障不影响DCS运行
✅ 物理隔离 - 通过网络分段和数据隔离实现
快速开始
# 克隆仓库
git clone https://github.com/yourusername/industrial-ai-assistant.git
cd industrial-ai-assistant
# 安装依赖
pip install -r requirements.txt
# 运行示例
python examples/chemical_process/demo.py文档
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
License
This project is licensed under the MIT License - see LICENSE file for details.
Acknowledgments
Model Context Protocol (MCP) - Anthropic's open protocol
Composio - Integration framework
Industrial automation community for domain knowledge patterns
Contact
Issues: GitHub Issues
Discussions: GitHub Discussions
Disclaimer: This is an open-source research project. Use in production environments at your own risk and only after thorough security assessment.
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