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Al Deep Learning-Based Sorting Machine
Features :
1. Ultra-high precision sorting
Multi-dimensional feature recognition: AI algorithms can analyze
multi-dimensional features such as color, texture, shape, and
surface defects (such as cracks and mildew) through deep learning,
and solve the problem of missed detection caused by traditional
color sorters relying on a single color threshold (such as
transparent foreign bodies or impurities of similar colors).
Complex scene adaptation: Convolutional neural network (CNN) is
used to deal with complex background noise, such as accurately
identifying mixed tea stems and normal leaves in tea sorting, and
the false positive rate can be reduced to less than 0.01%.
2. Dynamic adaptive optimization
E-learning capability: Using transfer learning technology, the
device can quickly fine-tune the model after the new material goes
live (e.g., the training time is reduced by 70% when migrating from
rice sorting to coffee bean sorting).
Environmental self-calibration: The optical correction algorithm is
integrated to compensate for light fluctuations or dust
interference in real time, ensuring the stability of sorting in the
continuous operation of the production line, and avoiding batch
quality fluctuations caused by environmental changes of traditional
equipment.
3. Revolution in efficiency and cost
Faster processing speed: The GPU-accelerated AI inference engine
supports image processing of more than 1,000 frames per second, and
with the high-speed valve array, the processing capacity of a
single machine can reach 20 tons/hour (40% higher than that of
traditional models).
Energy consumption optimization: Through reinforcement learning to
optimize the trigger strategy of the spray valve, the compressed
air consumption is reduced by 30%, and the annual energy saving
cost exceeds 150,000 yuan (taking the 24-hour production line as an
example).
Accepted Rejected