{"id":17127,"date":"2025-01-29T11:19:53","date_gmt":"2025-01-29T09:19:53","guid":{"rendered":"https:\/\/spatialworld.fi\/?page_id=17127"},"modified":"2025-02-05T17:11:04","modified_gmt":"2025-02-05T15:11:04","slug":"17076-2-2-2-3","status":"publish","type":"page","link":"https:\/\/spatialworld.fi\/fi\/17076-2-2-2-3\/","title":{"rendered":"RaspberryPi"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n
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🔧 Menestyst\u00e4 ja opittuja asioita: Edge Computing Raspberry Pi 5:ll\u00e4 ja FME Remote Enginell\u00e4! 🍓💻🦎<\/span><\/h1>\n<\/header>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”17201″ image_size=”medium_large” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”75%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n
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Meill\u00e4 on ilo ilmoittaa, ett\u00e4 Raspberry Pi 5:t\u00e4<\/span><\/strong>\u00a0ja\u00a0<\/span>FME:n Remote Engine\u00e4<\/span><\/strong>\u00a0k\u00e4ytt\u00e4v\u00e4 reunalaskentaratkaisumme\u00a0<\/span>on virallisesti\u00a0<\/span>k\u00e4yt\u00f6ss\u00e4<\/span><\/strong>\u00a0! 🎉<\/span><\/p>\n

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Prosessi:<\/strong><\/p>\n

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  1. Kehyskaappaus videovirroista:<\/strong>\u00a0K\u00e4yt\u00e4mme Raspberry Pi:t\u00e4 kehysten poimimiseen live-videosy\u00f6tteist\u00e4 reaaliajassa, valmiina v\u00e4litt\u00f6m\u00e4\u00e4n k\u00e4sittelyyn.<\/li>\n
  2. Kuvan valmistelu FME Remote Enginen avulla:<\/strong>\u00a0FME Engine k\u00e4sittelee kuvat paikallisesti Pi:ll\u00e4 optimoiden ne my\u00f6hemp\u00e4\u00e4 analysointia varten.<\/li>\n
  3. Objektintunnistus YOLOn avulla Darknetin kautta:<\/strong>\u00a0Darknetiin integroidut edistyneet AI-mallit (YOLO) tunnistavat kuvissa olevat kohteet ja varmistavat avainelementtien tarkan havaitsemisen.<\/li>\n
  4. Ulostulon tarkentaminen FME Remote Enginen avulla:<\/strong>\u00a0FME tarkentaa ja suodattaa edelleen havaitsemistuloksia, mik\u00e4 parantaa tietojen tarkkuutta ja asianmukaisuutta.<\/li>\n
  5. Ehdollinen raportointi FME Flow’lle:<\/strong>\u00a0Kattavat raportit l\u00e4hetet\u00e4\u00e4n FME Flow’n p\u00e4\u00e4esiintym\u00e4\u00e4n vain, kun tietyt objektit havaitaan m\u00e4\u00e4ritellyll\u00e4 todenn\u00e4k\u00f6isyydell\u00e4, mik\u00e4 v\u00e4hent\u00e4\u00e4 tarpeetonta tiedonsiirtoa.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/figure>\n<\/div>\n[\/vc_column_text][image_with_animation image_url=”17200″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

    Mutta matkalla ei ollut haasteita. Vaikka Raspberry Pi 5 -laitteisto on vaikuttava, kohtasimme odottamattomia vaikeuksia k\u00e4ytt\u00f6j\u00e4rjestelmien kanssa.<\/p>\n

    💡\u00a0Mit\u00e4 opimme:<\/strong><\/p>\n

    ✅\u00a0Edge Workflow Success:<\/strong>\u00a0Pi:n parannettu suorituskyky yhdistettyn\u00e4 FME:n tehokkaaseen tiedonk\u00e4sittelyyn loistaa todella reunalaskentaskenaarioissa.<\/p>\n

    ⚠️\u00a0Edess\u00e4 olevat haasteet:<\/strong><\/p>\n

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    • Raspberry Pi OS:<\/strong><\/li>\n<\/ul>\n
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      1. Yhteensopivuusongelmat:<\/strong>\u00a0Jotkin FME:n vaatimat riippuvuudet ja kirjastot eiv\u00e4t olleet helposti saatavilla tai yhteensopivia.<\/li>\n
      2. Asennuksen esteet:<\/strong>\u00a0FME Enginen asentaminen Raspberry Pi OS:\u00e4\u00e4n oli monimutkaisempaa kuin odotettiin.<\/li>\n<\/ol>\n
          \n
        • Ubuntu 24.04 ARM:<\/strong><\/li>\n<\/ul>\n
            \n
          1. Kameran yhteensopivuus:<\/strong>\u00a0Raspberry Pi -kamera ei ollut helposti yhteensopiva kuljettajien erojen vuoksi.<\/li>\n<\/ol>\n
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            ✨\u00a0Ratkaisumme:<\/strong><\/p>\n

            Siirtyimme\u00a0Ubuntu 24.04 ARM:<\/strong>\u00a0iin ja mukautimme videotulomme k\u00e4ytt\u00e4m\u00e4ll\u00e4\u00a0YouTube-streamia<\/strong>\u00a0.<\/p>\n

              \n
            • Saumaton integrointi:<\/strong>\u00a0Ubuntun yhteensopivuus FME:n kanssa mahdollisti sujuvamman asennuksen ja toiminnan.<\/li>\n
            • Parannettu tuki:<\/strong>\u00a0Laaja dokumentaatio ja yhteis\u00f6n tuki Ubuntulle ARM:ssa auttoivat meit\u00e4 voittamaan esteet nopeasti.<\/li>\n<\/ul>\n
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              🌟\u00a0L\u00e4hestymistapamme edut:<\/strong><\/p>\n

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              • Reaaliaikainen tietojenk\u00e4sittely:<\/strong>\u00a0Suorittamalla laskelmia suoraan Raspberry Pi:ll\u00e4 saamme v\u00e4litt\u00f6mi\u00e4 n\u00e4kemyksi\u00e4 ilman viiveit\u00e4.<\/li>\n
              • Kaistanleveyden optimointi:<\/strong>\u00a0Paikallinen k\u00e4sittely v\u00e4hent\u00e4\u00e4 tarvetta siirt\u00e4\u00e4 suuria tietom\u00e4\u00e4ri\u00e4 verkon yli, mik\u00e4 s\u00e4\u00e4st\u00e4\u00e4 kaistanleveytt\u00e4.<\/li>\n
              • Kustannustehokas skaalautuvuus:<\/strong>\u00a0Raspberry Pi 5:n kaltaisten kohtuuhintaisten laitteistojen avulla on helppo skaalata reunalaskentasolmuja tarpeen mukaan.<\/li>\n
              • Parannettu joustavuus:<\/strong>\u00a0FME:n Remote Engine k\u00e4sittelee erilaisia \u200b\u200btietomuotoja ja ty\u00f6nkulkuja, mink\u00e4 ansiosta voimme mukautua nopeasti erilaisiin skenaarioihin.<\/li>\n
              • Parannettu yksityisyys ja turvallisuus:<\/strong>\u00a0Tietojen k\u00e4sittely reunalla minimoi arkaluontoisten tietojen paljastumisen verkoissa.<\/li>\n
              • Yksinkertaistettu anturien integrointi:<\/strong>\u00a0Mahdollisuus lis\u00e4t\u00e4 helposti oheislaitteita, kuten USB-kameroita, GPS-moduuleja ja l\u00e4mp\u00f6tila-antureita, laajentaa tiedonkeruun laajuutta.<\/li>\n<\/ul>\n
                \n

                🚀\u00a0Eteenp\u00e4in:<\/strong><\/p>\n

                  \n
                • Testaus USB-kameroilla:<\/strong>\u00a0Suunnittelee integroida USB-kameroita, joilla on Ubuntussa yleisemmin tuetut ajurit.<\/li>\n
                • Lis\u00e4antureiden tutkiminen:<\/strong>\u00a0Laajennamme kokoonpanoamme sis\u00e4lt\u00e4m\u00e4\u00e4n GPS:n, l\u00e4mp\u00f6tilan ja paljon muuta kattavaa tiedonkeruuta varten.<\/li>\n<\/ul>\n
                  \n

                  🔑\u00a0Key Takeaway:<\/strong>\u00a0K\u00e4ytt\u00f6j\u00e4rjestelm\u00e4ll\u00e4 on ratkaiseva rooli reunalaskentaprojektien onnistumisessa. Jos uskallat samanlaiselle alueelle, harkitse Ubuntu ARM:n k\u00e4ytt\u00f6\u00e4 Raspberry Pi -laitteessa sujuvamman kokemuksen saamiseksi.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"

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