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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Поддерживает ввод нескольких поисковых фраз (по одной на строку). При поиске обеспечивает поддержку морфологии русского и английского языка
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Применить Всего найдено 2. Отображено 2.
06-06-2023 дата публикации

Intelligent marking control system

Номер: CN116215088A
Принадлежит:

The invention discloses an intelligent marking control system, belongs to the field of marking control, and solves the problem of how to process defect position information and carry out defect marking according to a processing result after the defect position information of a polarizing film is obtained. The system comprises an information acquisition module, an image information processing module, a marking control module, a driving module and a jet printing module. Through combination of an information acquisition module and an image information processing module, one or more groups of defect contour coordinate sequences of each area code under a polarizing film coordinate system are obtained; the marking control module analyzes and processes the defect contour coordinate sequence to obtain the ink-jet small hole number and the ink-jet marking frequency for corresponding defect marking, and a marking control instruction is generated; therefore, the subsequent driving module and the jet ...

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30-05-2023 дата публикации

High-speed online marking method based on dot-matrix marking method

Номер: CN116176148A
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The invention discloses a high-speed online marking method based on a dot-matrix marking method, and relates to the technical field of dot-matrix marking. Information of all commodities needing to be marked and corresponding marking information are collected in advance; training a neural network model for identifying commodity types by using the commodity pictures; the method comprises the following steps: pre-collecting a picture of each commodity package, and marking a position needing to be marked on the picture; judging the type of the commodity on the marking machine by using a neural network model, and comparing whether the marking position of the commodity is correct or not by using a picture comparison technology; processing the commodity according to a position comparison result; the dot-matrix marking machine reads a marking picture corresponding to the commodity type from the data storage device, and marks the marking picture on a commodity package by using a dot-matrix marking ...

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