Introduction

The small-sized distilled models of DeepSeek-R1 are fine-tuned using chain-of-thought data generated by DeepSeek-R1, marked with <think>...</think> tags, inheriting the reasoning capabilities of R1. These fine-tuned datasets explicitly include reasoning processes such as problem decomposition and intermediate deductions. Reinforcement learning has aligned the distilled model's behavior patterns with the reasoning steps generated by R1. This distillation mechanism allows small models to maintain computational efficiency while obtaining complex reasoning abilities near those of larger models, which is of significant application value in resource-constrained scenarios. For instance, the 14B version achieves 92% of the code completion of the original DeepSeek-R1 model. This article introduces the DeepSeek-R1 distilled model and its core applications in industrial edge computing, summarized in the following four directions, along with specific implementation cases:

Predictive Maintenance of Equipment

Technical Implementation

Sensor Fusion:

Integrate vibration, temperature, and current data from PLCs via the Modbus protocol (sampling rate 1 kHz).

Feature Extraction:

Run Edge Impulse on Jetson Orin NX to extract 128-dimensional time-series features.

Model Inference:

Deploy the DeepSeek-R1-Distill-14B model, inputting feature vectors to generate fault probability values.

Dynamic Adjustment:

Trigger maintenance work orders when confidence > 85%, and initiate a secondary verification process when < 60%.

Relevant Case

Schneider Electric deployed this solution on mining machinery, reducing false positive rates by 63% and maintenance costs by 41%.

Running DeepSeek R1 Distilled Model on InHand AI Edge Computers

Enhanced Visual Inspection

Output Architecture

Typical deployment pipeline:

camera = GigE_Vision_Camera(500fps) # Gigabit industrial camera
frame = camera.capture() # Capture image
preprocessed = OpenCV.denoise(frame) # Denoising preprocessing
defect_type = DeepSeek_R1_7B.infer(preprocessed) # Defect classification
if defect_type != 'normal':
PLC.trigger_reject() # Trigger sorting mechanism

Performance Metrics

Processing Delay:

82 ms (Jetson AGX Orin)

Accuracy:

Injection molded defect detection reaches 98.7%.

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Process Flow Optimization

Key Technologies

Natural Language Interaction:

Operators describe equipment anomalies via voice (e.g., "Extruder pressure fluctuation ±0.3 MPa").

Multimodal Reasoning:

The model generates optimization suggestions based on equipment historical data (e.g., adjusting screw speed by 2.5%).

Digital Twin Verification:

Parameter simulation validation on the EdgeX Foundry platform.

Implementation Effect

BASF’s chemical plant adopted this scheme, achieving a 17% reduction in energy consumption and a 9% increase in product quality rate.

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Instant Retrieval of Knowledge Base

Architecture Design

Local Vector Database:

Use ChromaDB to store equipment manuals and process specifications (embedding dimension 768).

Hybrid Retrieval:

Combine BM25 algorithm + cosine similarity for query.

Result Generation:

R1-7B model summarizes and refines retrieval results.

Typical Case

Siemens engineers resolved inverter failures through natural language queries, reducing average processing time by 58%.

Deployment Challenges and Solutions

Memory Limitations:

Utilized KV Cache quantization technology, reducing the memory usage of the 14B model from 32GB to 9GB.

Ensuring Real-Time Performance:

Stabilized single inference latency to ±15 ms through CUDA Graph optimization.

Model Drift:

Weekly incremental updates (transmitting only 2% of parameters).

Extreme Environments:

Designed for wide temperature ranges of -40°C to 85°C with IP67 protection level.

Conclusion

Current deployment costs have now decreased to $599/node (Jetson Orin NX), with scalable applications forming in sectors such as 3C manufacturing, automotive assembly, and energy chemistry. Continuous optimization of the MoE architecture and quantization technology is expected to enable the 70B model to run on edge devices by the end of 2025.

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