The intelligence level of AI optical selection machine is specifically reflected in self-learning and model optimization, multi-sensor fusion and intelligent recognition, intelligent operation and remote control, etc. The following is a detailed introduction:
Self directed learning and model optimization
Self learning based on big data: The AI light selection machine adopts a neural network algorithm framework and trains an exclusive model based on a millions level real-world database, which can achieve self-directed learning of incoming materials. It can automatically learn its features based on input material data, without the need for frequent manual parameter adjustments, and has strong adaptability to new materials.
Cloud one click intelligent upgrade: Some AI light selection machines can achieve one click intelligent upgrade through the cloud, which can adaptively adjust the recognition model according to the dynamic changes of regional material composition. It can also remotely optimize algorithms and update recognition libraries through OTA to maintain high performance and accuracy of the equipment.
Multi sensor fusion and intelligent recognition
Multi dimensional information collection: AI optical sorting machine integrates multiple sensor technologies, such as visible light, X-ray, near-infrared, fluorescence, laser, hyperspectral, metal detector, etc., to obtain multi-dimensional information such as material color, texture, chemical composition, internal structure, etc. The hyperspectral camera it is equipped with can perform full spectrum recognition of plastics, waste paper, waste textiles, etc. through 256 bands, accurately distinguishing various materials such as PC, PVC, PETG, etc.
Intelligent decision-making and precise recognition: Using fuzzy logic and deep neural network algorithms, decision level fusion of multi-source heterogeneous data is achieved to achieve precise identification and classification of materials. It can simultaneously recognize hundreds of materials, and even recognize composite plastic packaging composed of multiple material combinations, as well as waste paper with different fiber densities.
Intelligent operation and remote control
Real time device monitoring: Through the combination of edge end cloud and continuous monitoring of various sensor data, AI optical selection machine can achieve real-time monitoring of device operation status, timely discovering equipment faults, hidden dangers and abnormal situations.
Predictive maintenance: Based on real-time monitoring data and big data analysis, AI optical selection machines can predict the occurrence time of equipment failures, formulate maintenance plans in advance, carry out preventive maintenance, reduce equipment downtime, and lower maintenance costs.
Remote control: Operators can adjust parameters, upgrade programs, diagnose and handle faults of AI optical selection machines through remote terminals, achieving remote intelligent management of equipment and improving management efficiency and convenience.
High speed processing and intelligent sorting strategy
High speed scanning and processing: The AI optical sorting machine is equipped with a high-speed scanning system and advanced algorithms, which can quickly identify and sort materials, greatly improving processing efficiency.
Intelligent sorting strategy optimization: Based on the characteristics and sorting requirements of materials, AI optical sorting machines can intelligently adjust sorting strategies, such as the opening and closing time and quantity of air valves, to achieve precise sorting of materials of different sizes and types.