{"id":29620,"date":"2025-01-14T11:20:06","date_gmt":"2025-01-14T10:20:06","guid":{"rendered":"https:\/\/hitech.at\/exploiting-the-potential-of-generative-ai-in-industry\/"},"modified":"2026-10-22T15:12:32","modified_gmt":"2026-10-22T13:12:32","slug":"exploiting-the-potential-of-generative-ai-in-industry","status":"publish","type":"post","link":"https:\/\/hitech.at\/bg\/exploiting-the-potential-of-generative-ai-in-industry\/","title":{"rendered":"\u041f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044a\u0442 \u043d\u0430 \u0433\u0435\u043d\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u0438\u044f \u0438\u0437\u043a\u0443\u0441\u0442\u0432\u0435\u043d \u0438\u043d\u0442\u0435\u043b\u0435\u043a\u0442 \u0432 \u0438\u043d\u0434\u0443\u0441\u0442\u0440\u0438\u044f\u0442\u0430"},"content":{"rendered":"\n

Недостигът на квалифицирани работници и конкурентният натиск се усещат в ежедневния трудов живот. Сътрудничеството между човек и машина може да отвори напълно нови възможности тук. Системите за помощ, базирани на генеративен AI, могат например да генерират код и да поддържат операторите с практически инструкции за технически задачи. По този начин генеративният AI може да промени индустрията и да отприщи потенциал за ефективност и устойч

ивост.Siemens Industrial Copilot е асистент, подкрепен от генеративния AI. Ядрото му се крие в интелигентното използване на процесни и машинни данни, които могат да бъдат записани ефективно и по стандартизиран начин с помощта на интерфейса OPC UA. Тази технология гарантира, че оперативните данни могат да бъдат безпроблемно прехвърляни и анализирани в реално време, като помага на операторите да реагират по-бързо на различни състояния на машината и потенциални проблеми. В същото време ко-пилотът дава възможност да се предоставят важни документи (ръководства или стандартни оперативни процедури) директно на машината. Това хранилище за цифрови документи осигурява на операторите поддръжка и решава проблеми. AI може също да предостави информация за контекста въз основа на машинни данни и документи, което повишава безопасността и ефективността.<\/p>\n\n\n\n

\"\"© Siemens<\/span><\/span><\/figure>\n\n\n\n

Системите за помощ, базирани на генеративен AI, например, могат да генерират код.<\/p>\n\n\n\n

Чрез използването на най-съвременни технологии като изкуствен интелект и анализ на данни в реално време машините и процесите стават по-интелигентни, по-устойчиви и по-лесни за използване. Примери за приложения за съвместния пилот включват:<\/p>\n\n\n\n

Енергиен съвет<\/strong>ник: Открива аномалии в енергийните данни на производствените съоръжения и автоматично идентифицира мерки, които предлагат потенциални икономии. „Оперативният екип е подкрепен с оптимизирани енергийни доклади и показва как консумацията на енергия може да бъде устойчиво намалена, което спестява разходи и защитава околната среда в дългосрочен план“, казва Даниела Боргман (ръководител на проекта R&D).<\/p>\n\n\n\n

Асистент за смяна и операции:<\/strong> Интерактивният, базиран на AI оперативен асистент подкрепя оперативния екип, като действа като учен за лични данни. Той осигурява достъп до цялата необходима информация по време на смени, предоставя удобни за потребителя резюмета и отчети за KPI и свежда до минимум усилията, свързани с промените в смяната. В допълнение асистентът корелира поведението на различни машини и производствени линии, за да придобие по-задълбочени прозрения и да увеличи оперативната надеждност. „Интегрираме иновативни езикови модели в индустриалните системи. Това позволява на оперативния екип да получава съвети за оптимизация чрез чат\", казва Томас Блумауер-Хиесъл, изследовател в Siemens Austria.<\/p>\n\n\n\n

Големите езикови модели (LLM) могат да „халюцинират“, т.е. да генерират правдоподобно звучаща, но невярна информация. Разширеното генериране на извличане (RAG) помага чрез включване на външни знания от надеждни източници, което подобрява точността. Фината настройка на LLM го оптимизира специално за индустриалните изисквания, което увеличава уместността и надеждността на отговорите в експлоатация.“

Siemens предлага мощен GPU клъстер (компютърен клъстер за много бързи изчисления) на място. Това позволява моделите на изкуствен интелект да бъдат адаптирани към специфичните за компанията изисквания бързо и в съответствие с разпоредбите за защита на данните\", обяснява Даниел Шал, ръководител на изследователската група за разпределени системи за изкуствен интелект в Siemens Austria. Компаниите се възползват от повишената сигурност на данните и възможността ефективно да адаптират приложенията за изкуствен интелект към техните специфични нужди.<\/p>\n\n\n\n

\n

Siemens предлага мощен графичен клъстер на място. Това означава, че моделите на изкуствен интелект могат да бъдат адаптирани към специфичните за компанията изисквания бързо и в съответствие с разпоредбите за защита на данните.<\/p>\n\n\n\n

Даниел Шал, ръководител на изследователската група за разпределени системи за изкуствен интелект, Siemens Австрия<\/strong><\/strong><\/p>\n\n\n\n

\"\"© Siemens<\/span><\/span><\/figure>\n<\/blockquote>\n\n\n\n

Въпреки огромния потенциал на Индустриалния Copilot компаниите често се сблъскват с препятствия при интегрирането му в съществуващи системи (за управление, контрол, MES, PLM). Siemens решава този проблем – стандартизираните интеграционни чертежи позволяват внедряването и мащабирането на копилотни решения (например чрез интегриране в системи за управление като SIMATIC PCS7, PCS neo, Siemens OpCenter Execution и др.).<\/p>\n\n\n\n

Ефективното внедряване на Siemens Industrial Copilot на крайно устройство позволява безпроблемна интеграция в системите за автоматизация, без да се налага данните да напускат помещението на магазина. Това защитава поверителността и позволява неограничена работа без прекъсвания в мрежата и проблеми с интернет връзката.<\/p>\n\n\n\n

Индустриалният Copilot на Siemens използва данни в реално време и съответни документи, за да поддържа операторите, да минимизира грешките и да оптимизира работата на машината. Адаптивното насочване подобрява прецизността и ефективността, докато копилотът се учи от взаимодействията и непрекъснато оптимизира препоръките. Фината настройка със специфичните за компанията данни прави копилота по-прецизен и може да осигури поддръжка чрез персонализирани източници на данни и редовни проверки на производителността.<\/p>\n<\/body><\/html>\n","protected":false},"excerpt":{"rendered":"

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