Problems Solved
What is IoT+AI
IoT is responsible for collecting data from the physical world, such as temperature, vibration, current, output, location, and device status. AI is responsible for understanding this data, discovering patterns, predicting trends, and assisting decision-making.
When combined, devices no longer merely report status passively but can form prediction, early warning, optimization, and automatic handling capabilities.
| Stage | Role | Typical Capabilities |
|---|---|---|
| Collection | Acquire real on-site data | Sensors, PLC, RFID, industrial cameras, smart meters |
| Analysis | Understand equipment operating patterns | Time-series analysis, anomaly detection, root cause analysis, trend prediction |
| Action | Transform analysis into business actions | Automatic alerts, strategy adjustments, maintenance work orders, energy optimization |
Core Capabilities
| Capability | Description |
|---|---|
| Multi-protocol Data Collection | Supports mainstream IoT protocols such as MQTT, Modbus, OPC UA, and HTTP. |
| Edge Computing | Performs local data preprocessing to reduce latency and bandwidth pressure. |
| AI Predictive Analytics | Predictive maintenance, anomaly detection, and trend analysis based on time-series data. |
| Real-time Monitoring Dashboard | Displays device status, energy consumption, line efficiency, alerts, and processing progress. |
| Secure and Reliable | Device authentication, encrypted transmission, permission control, and operation auditing. |
| Device Digital Twin | Builds device models for simulation, comparison, and optimization of operational strategies. |
Industry Application Scenarios
| Industry | Scenario | Value |
|---|---|---|
| Manufacturing | Predictive maintenance | Using vibration, temperature, current, and other data to identify equipment failures in advance, reducing unplanned downtime. |
| Energy Management | Intelligent energy optimization | Analyzing energy consumption patterns, identifying high-consumption areas, and providing optimization strategies. |
| Quality Control | AI Visual Inspection | Using industrial cameras and vision models to detect product defects in real time. |
| Warehousing & Logistics | Intelligent warehouse management | Combining RFID and sensors to optimize bin locations, inbound/outbound, and picking paths. |
Reference Architecture
| Layer | Content |
|---|---|
| Perception Layer | Sensors, PLC, RFID, cameras, smart meters |
| Network Layer | 5G, Wi‑Fi 6, LoRa, NB‑IoT, edge gateways |
| Platform Layer | IoT platform, data middle platform, AI engine, rule engine |
| Application Layer | Monitoring dashboard, predictive maintenance, energy optimization, quality inspection |
Data Dashboard Examples
| Metric | Example | Usage |
|---|---|---|
| Device Online Rate | 99.7% | Assess device connection stability and on-site operational health. |
| Fault Warning | 3 units pending | Helps operations schedule inspections and spare parts in advance. |
| Today's Energy | 1,280 kWh | Compare historical energy usage and identify abnormal consumption. |
Delivery Process
Service Pricing Reference
| Edition | Applicable Scope | Service Rate |
|---|---|---|
| Pilot | Single production line or scenario pilot, suitable for quickly validating feasibility. | Starting from 5% of project budget |
| Standard | Full production line deployment, including data collection, analysis, and visualization. | Starting from 8% of project budget |
| Enterprise | Plant-wide IoT+AI platform, supporting digital twins and edge computing. | Starting from 12% of project budget |
FAQ
Can legacy equipment be connected to IoT?
Yes. External sensors, OPC gateways, or data acquisition boxes can equip legacy equipment with data collection capabilities—no need to replace the equipment in most cases.
How long is the typical deployment cycle?
A pilot project usually takes 2 to 4 weeks; a full solution typically 4 to 12 weeks, depending on the number of devices, protocol complexity, and on-site network conditions.
How is data security ensured?
Private deployment is supported, and data can remain within the enterprise intranet. Transport encryption, device authentication, permission control, and operation auditing can also be configured.
Does the existing production line need to be modified?
In most scenarios, no large-scale modification is needed. The solution prioritizes non‑intrusive collection and edge gateways to minimize disruption to ongoing production.