Physical AI 藍圖：連接數碼智能與工業控制

The Architecture of Physical AI - Why an Engine Based Approach is Critical to Enterprise IoT
Enterprise IoT has suffered with fragmentation as a challenge over many years. Often systems are deployed with multiple point solutions for identification via sensors, asset tracking & dashboards for analytics. These systems help generate telemetry data but don't translate signals into accurate, automated actions in the physical world.
By scaling up systems in industrial processes we need to adopt a modular engine based approach.

Physical AI Action Engine
(Automated interlocks, Work orders, Robotics)
↑
Verified Context & Intent
AI Decision Engine
(Anomaly Detection, Risk Prediction, Optimization)
↑
Normalized Telemetry
Sensing Engine
(Condition monitoring, Environmental & Process)
↑
Identified Entities
Identification Engine
(RFID, BLE, RTLS, Staff/Asset ID)
The 4 Core Engines
Modern systems in the physical world utilize 4 core engines to take telemetry from the real world and create deterministic actions.

Identification Engine (Identity / Context)
A system needs clear identification of entities within a process or asset before it can start reasoning.
Purpose: Capture unique identities & locate them across equipment, stock & staff. This can be done by:
RFID/Passive RFID
BLE
UWB RTLS
Computer Vision
Captures what assets/people are present in a process & their location.

Sensing Engine (Condition Telemetry)
Without having identification we have no state information. This engine will take information from the identification engine & add environmental & mechanical data.
Purpose: Capture complex signals from multiple sensors within an unpredictable industrial environment.
Vibration Sensors
Thermal
Humidity/Pressure
Power Usage

Captures what the asset is doing in real time.

AI Decision Engine (Reasoning & Predictive)
The intelligence engine takes the real time identification & condition data & uses advanced ML models to reason across data & identify anomalies.
Purpose: Use intelligent data to reason across patterns within complex processes to form recommendations.
Edge MLOps
Digital Twins
Anomaly Detection
Risk Modelling

Captures what will happen next & makes recommendations on optimization.

Physical AI Action Engine
To make any decision useful within a process it needs to result in a coordinated action. The last engine will provide a link from intelligent decisions down into physical processes ensuring safety standards.

[Identify] [Sense] → [Context] → [Safety Interlocks] → [Action] → [Verify]
Purpose: Capture intelligent outputs from the models & perform actions within the physical world such as generating work orders automatically.
PLC Integration
AMR Orchestration
Automated Dispatch
Safety Interlocks
Performs actions to a limited extent in the physical world, verifies the outcome & updates the process model.

Enterprise Matrix
When trying to build or evaluate a complete Physical AI solution you need to ensure that the correct capabilities are in place at each level.
Engine Purpose Inputs Outputs Human Element

Identification Engine Capture tag data from sensors or camera + location data Asset/person located High (Automated)
Sensing Engine Capture raw IoT data from sensors / edge sensors Normalised time series data Low (Automated)
AI Decision Engine Capture data from identification & sensing engines to form context Recommendations Low (Threshold)
Physical AI Action Engine Capture data from decisions & safety interlocks Modify asset, Work Order or Dispatch Robot High (Human)
Human Element
To move towards automated actions in the physical world you need constant human oversight. With a lot riding on equipment safety, staff safety or high value manufacturing lines you can't simply make predictions with a black box process.

Responsible deployment of Physical AI will keep systems within a defined envelope. The system will still provide guided recommendations, alerts & work orders but fully automated physical changes are only allowed within a low risk envelope.

By building modular Physical AI solutions you can speed up deployment within enterprise environments. If you're looking to explore new areas of niche technology incubation & deployment within the industry, find out more from Aperture Venture Studio’s publications.