FISH launches fabric intelligent search

Apr 04, 2023  |  by


The Intertextile Shanghai Apparel Fabrics-Spring Editon was grandly held at the National Exhibition and Convention Center in Shanghai from March 28th to 30th. Two heavyweight products of FISH (Fabric Intelligence Search Hub), FISE (Fabric Intelligence Search Engine) and PLSS (Product Library Selecting System), made their simultaneous debut at the Digital Fashion Innovation Space and corporate booths. The booth layout and intelligent products, including smart cabinets, automatic guided vehicles (AGVs), robots, etc., filled with a sense of technology, attracted strong attention and extensive consultation from buyers and exhibitors.


FISH (Fabric Intelligence Search Hub) has a physical sample collection of the entire textile product line, which is transformed into a large database through surface feature extraction imaging technology defined by standards. Continuous machine deep learning has achieved a fine-grained fabric intelligent search engine, which can be applied to multiple fields such as fabric variety search, fabric pattern search, and fabric dyeing and printing finished product point search. At the booth of FISH, the Product Library Selecting System (PLSS) is also very eye-catching. It is understood that the PLSS can easily achieve intelligent warehousing, inventory transfer, and out-of-warehouse selection of fabric samples. After finding matching spot inventory information through PLSS, the system can directly send physical samples to the demander, helping companies reduce capital occupation caused by inventory backlog and avoiding reinvestment in weaving dyeing and printing production.

The pursuit of fashion requires novel products, fast delivery and small quantity. This creates a contradiction with existing technological bottlenecks, cost control, process management, and quality standards. Many product testing indicators are difficult to meet, especially data indicators, which are critical to determining whether an order can be delivered.



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