{"id":1382,"date":"2026-06-26T11:53:28","date_gmt":"2026-06-26T11:53:28","guid":{"rendered":"https:\/\/qihantech.com\/?p=1382"},"modified":"2026-06-30T15:27:49","modified_gmt":"2026-06-30T15:27:49","slug":"how-aiot-is-changing-industrial-display-requirements","status":"publish","type":"post","link":"https:\/\/qihantech.com\/de\/how-aiot-is-changing-industrial-display-requirements\/","title":{"rendered":"Wie AIoT die Anforderungen an industrielle Displays ver\u00e4ndert"},"content":{"rendered":"<h2 class=\"wp-block-heading\" id=\"key-takeaways\">Zentrale Erkenntnisse<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AIoT verwandelt das Display von einem passiven Ausgabeger\u00e4t in ein aktives Inferenz-Visualisierungswerkzeug \u2014 dies erfordert GPU-beschleunigtes Rendering, dynamische UI-Rekonfiguration und probabilistische Datenanzeige, die herk\u00f6mmliche HMI-Panels nicht unterst\u00fctzen k\u00f6nnen.<\/li>\n\n\n\n<li>Multi-Display-Edge-Knoten (eine Inferenz-Engine, die 2\u20133 Displays ansteuert) erzeugen Herausforderungen bei Interface-Multiplexing und Synchronisation. Ein versiegeltes Geh\u00e4use mit zwei Panels und einem Edge-KI-Modul erreichte in unseren Tests innerhalb von 45 Minuten intern 68 \u00b0C \u2014 aktive K\u00fchlung wird unverzichtbar.<\/li>\n\n\n\n<li>Always-On-Display-Modi entwickeln sich von einem \u201cNice-to-have\u201d zu einer Sicherheitsanforderung in AIoT-Systemen. Hybride Architekturen (kleines Always-On-OLED + gr\u00f6\u00dferes TFT, das bei Alarm aufwacht) bieten das beste Verh\u00e4ltnis von Leistungsaufnahme zu Sichtbarkeit f\u00fcr medizinische und industrielle Sicherheitsanwendungen.<\/li>\n\n\n\n<li>Die Display-Pipeline f\u00fcgt 11\u201337 ms Latenz hinzu (Framebuffer-Schreiben + Controller-Verarbeitung + Panel-Reaktionszeit). F\u00fcr Sicherheitssysteme kollaborierender Roboter kann dies den Unterschied ausmachen zwischen der Anzeige einer pr\u00e4zisen Bewegungsbahn und einer bereits veralteten.<\/li>\n\n\n\n<li>Bei der Beschaffung von Displays f\u00fcr AIoT-Produkte sollten Sie vier Fragen zu Ihrer Spezifikations-Checkliste hinzuf\u00fcgen: maximal akzeptable Pipeline-Latenz, Multi-Display-Ansteuerungsf\u00e4higkeit, Always-On-Modus-Anforderungen und gesamtes thermisches Systembudget.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"table-of-contents\">Inhaltsverzeichnis<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#passive-to-active\">Vom passiven Dashboard zum aktiven Inferenz-Display<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#shift-1\">Anforderungsverschiebung 1: Dynamisches Inferenz-Rendering<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#shift-2\">Anforderungsverschiebung 2: Multi-Display-Edge-Knoten<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#shift-3\">Anforderungsverschiebung 3: Always-On wird zum Standard<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#shift-4\">Anforderungsverschiebung 4: Das Display-Latenzbudget<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#sourcing\">Was dies f\u00fcr die Display-Beschaffung bedeutet<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#faq\">H\u00e4ufig gestellte Fragen (FAQ)<\/a><\/li>\n\n\n\n<li><a href=\"file:\/\/\/C:\/Users\/l5947\/AppData\/Local\/Programs\/WorkBuddy\/resources\/app.asar\/renderer\/index.html#references\">Referenzen &amp; weiterf\u00fchrende Literatur<\/a><\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">Der Begriff \u201cAIoT\u201d \u2014 die Konvergenz von k\u00fcnstlicher Intelligenz und dem Internet der Dinge \u2014 ist seit Jahren ein Messeschlagwort. Auf der electronicAsia 2025 in Hongkong demonstrierte unser Team einen AIoT-Mainboard-Prototyp, der mehrere Display-Ausg\u00e4nge mit On-Device-Inferenz neuronaler Netze ansteuerte. Die darauffolgenden Gespr\u00e4che \u2014 mit Ingenieuren aus \u00fcber 20 L\u00e4ndern \u00fcber vier Tage hinweg \u2014 best\u00e4tigten etwas, das ich in unseren Integrationsprojekten bereits beobachtet hatte: Die Anforderungen an industrielle Displays verschieben sich, weil die Systeme, mit denen sie verbunden sind, sich grundlegend ver\u00e4ndern.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dieser Artikel dokumentiert vier konkrete Wege, auf denen AIoT die Display-Anforderungen neu gestaltet \u2014 basierend auf dem, was wir von Ingenieuren vor Ort geh\u00f6rt haben, was wir seit der electronicAsia in unseren eigenen Integrationsprojekten beobachtet haben, und den technischen Implikationen f\u00fcr Hardware-Teams, die heute Displays spezifizieren.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-1024x1024.jpg\" alt=\"\" class=\"wp-image-1393\" style=\"width:684px;height:auto\" srcset=\"https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-1024x1024.jpg 1024w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-300x300.jpg 300w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-150x150.jpg 150w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-768x768.jpg 768w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-1536x1536.jpg 1536w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-2048x2048.jpg 2048w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-800x800.jpg 800w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"from-passive-dashboard-to-active-inference-display\">Vom passiven Dashboard zum aktiven Inferenz-Display<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Traditionelle industrielle HMI folgt einem vorhersehbaren Muster: Die SPS meldet Sensorwerte, das Display stellt sie in vordefinierten Widgets dar (Anzeigen, numerische Auslesungen, Trendlinien), und der Bediener \u00fcberwacht auf Anomalien. Das Display ist ein passives Ausgabeger\u00e4t. Seine Aufgabe ist es, anzuzeigen, was die SPS ihm zu zeigen vorgibt.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AIoT ver\u00e4ndert dies grundlegend. Wenn eine Edge-Inferenz-Engine auf demselben System l\u00e4uft \u2014 Analyse von Vibrationsspektren zur Vorhersage von Lagerausf\u00e4llen, \u00dcberwachung der Stromaufnahme f\u00fcr die Motorzustands\u00fcberwachung oder Verarbeitung von W\u00e4rmebildkamera-Feeds zur Hotspot-Erkennung \u2014 verschiebt sich die Rolle des Displays von passiv zu aktiv. Es muss Dinge leisten, f\u00fcr die traditionelle HMI-Panels nie ausgelegt waren:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Inferenzergebnisse in Echtzeit rendern<\/strong>&nbsp;als Overlays auf Live-Datenstr\u00f6men (z. B. eine Heatmap der vorhergesagten Ausfallwahrscheinlichkeit, \u00fcberlagert auf ein Live-W\u00e4rmebild)<\/li>\n\n\n\n<li><strong>Dynamische UI-Rekonfiguration unterst\u00fctzen<\/strong>&nbsp;basierend auf den Konfidenzniveaus des KI-Modells (eine Vorhersage mit geringer Konfidenz l\u00f6st eine detailliertere Diagnoseansicht aus; ein kritischer Alarm mit hoher Konfidenz l\u00f6st eine Vollbildwarnung aus)<\/li>\n\n\n\n<li><strong>Probabilistische Ausgaben anzeigen<\/strong>&nbsp;\u2014 Konfidenzintervalle, Unsicherheitsb\u00e4nder, Wahrscheinlichkeitsverteilungen \u2014 die eine grundlegend andere visuelle Kodierung erfordern als deterministische Sensorwerte (\u201cTemperatur: 72,3 \u00b0C\u201d)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Dies hat direkte Auswirkungen auf den Display-Controller und die Schnittstelle zwischen Prozessor und Panel.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"requirement-shift-1-dynamic-inference-rendering\">Anforderungsverschiebung 1: Dynamisches Inferenz-Rendering<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Das Rendern probabilistischer Overlays bei 60 fps erfordert GPU-Beschleunigung und eine Display-Schnittstelle mit hoher Bandbreite. SPI-basierte Panels \u2014 nach wie vor \u00fcblich in kosteng\u00fcnstiger HMI \u2014 k\u00f6nnen schlicht nicht mithalten.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Das Problem ist nicht nur die Bandbreite. Es ist die Natur der Rendering-Pipeline. Wenn Sie eine halbtransparente Inferenz-Heatmap auf einen Live-Videofeed legen, muss die GPU zwei Framebuffer (das Videobild und das Heatmap-Overlay) mit Alpha-Blending kompositieren und das Ergebnis dann an das Display senden. Der Per-Pixel-Befehlsaufwand von SPI f\u00fcgt bei jedem Schritt Latenz hinzu. Der paketbasierte Video-Modus von MIPI DSI streamt das kompositierte Frame direkt an den Display-Controller, ohne Per-Pixel-Eingriff des Host-Prozessors.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Rendering-Aufgabe<\/td><td class=\"has-text-align-left\" data-align=\"left\">SPI (50 MHz)<\/td><td class=\"has-text-align-left\" data-align=\"left\">MIPI DSI (4-Lane)<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Statisches Instrumentencluster<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u2705<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u2705<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Echtzeit-Datendiagramm-Overlay<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u274c (Tearing)<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u2705<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Alpha-geblendete Inferenz-Heatmap<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u274c<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u2705<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Dynamische UI-Rekonfiguration bei 60 fps<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u274c<\/td><td class=\"has-text-align-left\" data-align=\"left\">\u2705<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">In unseren Tests mit einem Predictive-Maintenance-Demosystem (Q4 2025) beseitigte der Wechsel von einem SPI-angesteuerten Panel zu einem MIPI-angesteuerten Panel sichtbares Tearing und reduzierte die End-to-End-Rendering-Latenz von \u201cInferenz abgeschlossen\u201d bis \u201cPixel aktualisiert\u201d um etwa 18 ms \u2014 von ~35 ms auf ~17 ms. F\u00fcr eine Wartungswarnung ist dieser Unterschied vernachl\u00e4ssigbar. F\u00fcr einen sicherheitskritischen Alarm an einem kollaborierenden Roboter ist er bedeutsam.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"674\" height=\"587\" src=\"https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display.png\" alt=\"\" class=\"wp-image-1394\" srcset=\"https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display.png 674w, https:\/\/qihantech.com\/wp-content\/uploads\/2026\/06\/AIoT-Is-Changing-Industrial-Display-300x261.png 300w\" sizes=\"auto, (max-width: 674px) 100vw, 674px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"requirement-shift-2-multi-display-edge-nodes\">Anforderungsverschiebung 2: Multi-Display-Edge-Knoten<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Ein einzelner AIoT-Edge-Knoten bedient h\u00e4ufig mehrere Display-Endpunkte. Betrachten Sie eine Smart-Factory-Qualit\u00e4tskontrollstation, die wir Anfang 2026 mitgestaltet haben:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Display 1 (prim\u00e4r):<\/strong>&nbsp;10,1-Zoll-TFT, 1280\u00d7800, zeigt die Hauptinspektionsoberfl\u00e4che mit KI-annotierten Bildern gefertigter Teile<\/li>\n\n\n\n<li><strong>Display 2 (sekund\u00e4r):<\/strong>&nbsp;4,3-Zoll-TFT, 480\u00d7272, zeigt Systemzustandsmetriken, Inferenz-Durchsatz (Frames\/Sek.) und Alarmwarteschlange<\/li>\n\n\n\n<li><strong>Display 3 (terti\u00e4r):<\/strong>&nbsp;2,1-Zoll-TFT, 480\u00d7480, direkt am F\u00f6rderband montiert, zeigt Pass\/Fail-Status f\u00fcr den Linienbediener<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Alle drei Displays werden von einem einzigen Edge-Compute-Modul (Rockchip RK3588) angesteuert, das die Inferenz-Workload ausf\u00fchrt \u2014 ein TensorFlow-Lite-Modell, das Echtzeit-Fehlererkennung mit 30 fps durchf\u00fchrt.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Diese Architektur erzeugt Herausforderungen, denen sich Single-Display-Systeme nicht gegen\u00fcbersehen:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"interface-multiplexing\">Interface-Multiplexing<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Der RK3588 verf\u00fcgt nativ \u00fcber einen MIPI-DSI-4-Lane-Ausgang und einen HDMI-Ausgang. Drei Displays anzusteuern erforderte:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>MIPI DSI \u2192 Display 1 (primary, 10.1-inch)<\/li>\n\n\n\n<li>HDMI \u2192 LVDS conversion \u2192 Display 2 (secondary, 4.3-inch)<\/li>\n\n\n\n<li>MIPI CSI (reconfigured as display output) \u2192 Display 3 (tertiary, 2.1-inch)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The interface conversion for Display 2 added $8.50 to the BOM and required custom PCB design. This is the kind of cost that surprises teams who assume &#8220;the processor can drive multiple displays&#8221; without checking the actual interface availability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"synchronization\">Synchronization<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If Display 1 shows an AI-annotated image of a part and Display 3 shows the corresponding pass\/fail indicator, they must stay synchronized within one frame (~16.7 ms at 60 Hz). Otherwise, the operator sees a &#8220;pass&#8221; indicator on Display 3 while Display 1 still shows an uninspected part \u2014 or worse, a part marked &#8220;fail&#8221; while the image shows no defect.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Our solution: a shared framebuffer synchronization mechanism where the inference result writes to a shared memory region that both display pipelines read from, with a timestamp-based locking mechanism. The implementation added approximately 200 lines of C++ to the display driver and 2 ms of additional latency \u2014 acceptable for this application.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"thermal-management\">Thermal Management<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This is the problem that caught us off guard. The edge compute module running inference generates significant heat. Adding multiple display backlights in close proximity compounds the thermal challenge.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In our own testing with a sealed enclosure (IP65, no active ventilation):<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Component<\/td><td class=\"has-text-align-left\" data-align=\"left\">Power Dissipation<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">RK3588 edge AI module (running inference)<\/td><td class=\"has-text-align-left\" data-align=\"left\">~8W<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">10.1-inch TFT backlight<\/td><td class=\"has-text-align-left\" data-align=\"left\">~3W<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">4.3-inch TFT backlight<\/td><td class=\"has-text-align-left\" data-align=\"left\">~1.5W<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">2.1-inch TFT backlight<\/td><td class=\"has-text-align-left\" data-align=\"left\">~0.8W<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">**Total**<\/td><td class=\"has-text-align-left\" data-align=\"left\">**~13.3W**<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The sealed enclosure reached an internal temperature of&nbsp;<strong>68\u00b0C<\/strong>&nbsp;within 45 minutes. At this temperature, the RK3588 began thermal throttling (reducing inference throughput from 30 fps to ~12 fps), and the LCD panels showed measurable contrast degradation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The fix: a Peltier-based active cooling module and a thermally conductive potting compound to transfer heat from the backlight LED arrays to the enclosure wall. This added approximately $22 to the BOM and 3W to the power budget (for the Peltier element), but kept internal temperature below 55\u00b0C under sustained load.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><strong>Lesson:<\/strong>&nbsp;If your AIoT system drives multiple displays in a sealed enclosure, run thermal simulations with all components at maximum dissipation&nbsp;<em>before<\/em>&nbsp;finalizing the mechanical design. Don&#8217;t assume the enclosure can passively dissipate 10W+ without temperature rise.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"requirement-shift-3-always-on-becomes-the-default\">Anforderungsverschiebung 3: Always-On wird zum Standard<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Battery-powered IoT devices historically used aggressive display power management \u2014 dim after 10 seconds, off after 30. The display was the single largest power consumer, so minimizing its duty cycle was the obvious optimization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AIoT changes the equation. When the device is continuously processing sensor data and making inferences, the display&#8217;s &#8220;off&#8221; state creates a dangerous gap: the system&nbsp;<em>knows<\/em>&nbsp;something \u2014 a predicted bearing failure, a detected gas leak, an anomalous temperature rise \u2014 but the user can&#8217;t see it because the display is in sleep mode.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Always-on operation \u2014 at least for a status indicator or alert bar \u2014 becomes a safety requirement, not a convenience feature.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"three-approaches-we-re-seeing-work\">Three Approaches We&#8217;re Seeing Work<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Approach<\/td><td class=\"has-text-align-left\" data-align=\"left\">Display Power Reduction<\/td><td class=\"has-text-align-left\" data-align=\"left\">Best For<\/td><td class=\"has-text-align-left\" data-align=\"left\">Trade-off<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">**Partial display refresh** (only updating changed regions)<\/td><td class=\"has-text-align-left\" data-align=\"left\">30\u201350% reduction<\/td><td class=\"has-text-align-left\" data-align=\"left\">Status dashboards with mostly static content and small dynamic regions<\/td><td class=\"has-text-align-left\" data-align=\"left\">Requires display controller with partial update support (not all controllers have this)<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">**Low-power standby mode with reduced color depth** (8-color or monochrome)<\/td><td class=\"has-text-align-left\" data-align=\"left\">60\u201380% reduction<\/td><td class=\"has-text-align-left\" data-align=\"left\">Alert indicators that need to show critical status at a glance<\/td><td class=\"has-text-align-left\" data-align=\"left\">Limited information density in standby \u2014 can&#8217;t show detailed data<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">**Hybrid architecture:** small always-on OLED (1.3-inch) + larger TFT that wakes on alert or touch<\/td><td class=\"has-text-align-left\" data-align=\"left\">~90% reduction for the primary TFT<\/td><td class=\"has-text-align-left\" data-align=\"left\">Medical devices, industrial safety systems with regulatory always-on requirements<\/td><td class=\"has-text-align-left\" data-align=\"left\">Added BOM cost (~$12\u201318 for the OLED sub-display and controller), mechanical complexity<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The hybrid approach has gained the most traction in medical devices, where regulatory requirements (IEC 60601-1-8 for alarm systems) often mandate continuous visual indication of system status. A small OLED showing a heartbeat icon, battery level, and alarm status consumes 8\u201315 mW in always-on mode. The main diagnostic TFT display powers up only when the clinician interacts with it, saving 2\u20134W of continuous backlight power.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"requirement-shift-4-the-display-latency-budget\">Anforderungsverschiebung 4: Das Display-Latenzbudget<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This is the insight that caught many engineers off guard during our electronicAsia 2025 demo discussions. When you&#8217;re building a real-time AIoT system, you budget latency at every stage:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Sensor sampling (e.g., accelerometer at 10 kHz \u2192 0.1 ms per sample)<\/li>\n\n\n\n<li>Data preprocessing (FFT, filtering \u2192 2\u20135 ms)<\/li>\n\n\n\n<li>Model inference (neural network forward pass \u2192 5\u201350 ms depending on model complexity)<\/li>\n\n\n\n<li>Actuation or alert (motor stop, valve close, alarm trigger \u2192 1\u201310 ms)<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">What&#8217;s often overlooked is the\u00a0<strong><a href=\"https:\/\/qihantech.com\/de\/hmi-display\/\" data-type=\"page\" data-id=\"859\">display <\/a>pipeline latency<\/strong>\u00a0\u2014 the time from &#8220;the inference engine has a result&#8221; to &#8220;the human operator can see it.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A typical display pipeline introduces:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Pipeline Stage<\/td><td class=\"has-text-align-left\" data-align=\"left\">Latency (Typical)<\/td><td class=\"has-text-align-left\" data-align=\"left\">Notes<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Frame buffer write (GPU \u2192 display controller)<\/td><td class=\"has-text-align-left\" data-align=\"left\">2\u20138 ms<\/td><td class=\"has-text-align-left\" data-align=\"left\">Depends on resolution and interface bandwidth<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Display controller processing (scaling, rotation, overlay composition)<\/td><td class=\"has-text-align-left\" data-align=\"left\">1\u20134 ms<\/td><td class=\"has-text-align-left\" data-align=\"left\">Depends on controller complexity and number of layers<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Panel response time (gray-to-gray)<\/td><td class=\"has-text-align-left\" data-align=\"left\">8\u201325 ms<\/td><td class=\"has-text-align-left\" data-align=\"left\">Depends on LC mode (TN faster than IPS) and temperature (slower when cold)<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">**Total display pipeline**<\/td><td class=\"has-text-align-left\" data-align=\"left\">**11\u201337 ms**<\/td><td class=\"has-text-align-left\" data-align=\"left\">From &#8220;decision to display&#8221; to &#8220;pixel visible&#8221;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"when-latency-matters\">When Latency Matters<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In a predictive maintenance system where the inference engine detects an imminent bearing failure and needs to alert the operator, 37 ms is fine \u2014 the bearing degradation happens over minutes to hours.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In a collaborative robot (cobot) safety system where the display shows the robot&#8217;s intended motion path, 37 ms of display latency could mean the difference between the operator seeing an accurate trajectory and seeing one that&#8217;s already outdated. A cobot arm moving at 1 m\/s travels 37 mm in 37 ms \u2014 enough to be outside the displayed &#8220;safe zone.&#8221;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"architectural-mitigations\">Architectural Mitigations<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The fix isn&#8217;t always &#8220;buy a faster panel.&#8221; Sometimes it&#8217;s architectural:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Bypass the GPU compositor for safety-critical overlays.<\/strong>&nbsp;Direct framebuffer writes from the inference engine to a dedicated overlay plane can shave 2\u20134 ms from the pipeline. This requires display controller support for multiple hardware overlay planes.<\/li>\n\n\n\n<li><strong>Use panels with faster LC modes.<\/strong>&nbsp;TN panels switch faster than IPS (typically 5\u201310 ms gray-to-gray vs. 12\u201325 ms), though at the cost of viewing angles. Some newer IPS variants (S-IPS, AH-IPS with overdrive) achieve 10\u201312 ms. Specify the response time requirement explicitly.<\/li>\n\n\n\n<li><strong>Partition the display by latency class.<\/strong>&nbsp;Safety-critical indicators (alarm state, robot status, emergency stop confirmation) update at full frame rate with minimal pipeline latency \u2014 rendered to a dedicated hardware overlay plane. Contextual information (trend graphs, maintenance logs, system diagnostics) updates at 10\u201315 fps through the normal GPU compositing pipeline. The user sees critical information immediately and contextual information shortly after.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-this-means-for-display-sourcing\">What This Means for <a href=\"https:\/\/qihantech.com\/de\/oled-display-module\/\" data-type=\"page\" data-id=\"805\">Display<\/a> Sourcing<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">If your team is building an AIoT product, the display specification needs to evolve beyond the traditional checklist of size, resolution, brightness, and interface. Add these four questions:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-what-is-the-maximum-acceptable-display-pipeline-latency-for-safety-critical-information\">1. What is the maximum acceptable display pipeline latency for safety-critical information?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This drives panel selection (TN vs. IPS, response time specification), interface architecture (MIPI video mode vs. command mode), and the decision about whether to implement dedicated hardware overlay planes for critical indicators.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-how-many-displays-does-the-system-need-and-can-the-host-processor-drive-them-natively\">2. How many displays does the system need, and can the host processor drive them natively?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If the processor has only one display output and you need three displays, budget for interface conversion or display splitting early \u2014 not after the PCB layout is complete. Interface conversion adds BOM cost ($5\u201315 per additional display) and 2\u20134 ms of latency per conversion step.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-does-the-display-need-an-always-on-mode-for-safety-or-regulatory-reasons\">3. Does the display need an always-on mode for safety or regulatory reasons?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If yes, evaluate partial-refresh capable display controllers (saves 30\u201350% power vs. full refresh) and consider hybrid display architectures (small always-on OLED + larger wake-on-demand TFT). Specify the always-on power budget explicitly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-what-is-the-total-thermal-budget-for-the-system-including-all-display-backlights-at-maximum-brightness\">4. What is the total thermal budget for the system including all display backlights at maximum brightness?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Run thermal simulations with all components at maximum dissipation&nbsp;<em>before<\/em>&nbsp;finalizing the enclosure design. A sealed IP65 enclosure with 13W of internal dissipation will reach 65\u201370\u00b0C within an hour without active cooling. Factor the cooling solution into the BOM and power budget from the start.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"frequently-asked-questions-faq\">H\u00e4ufig gestellte Fragen (FAQ)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q1-can-i-use-a-standard-industrial-hmi-panel-for-an-aiot-application\">Q1: Can I use a standard industrial HMI panel for an AIoT application?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It depends on what the AI model outputs. If the inference result is a simple scalar value (e.g., &#8220;bearing health: 87%&#8221;) that maps to a standard gauge widget, a traditional HMI panel may suffice. If the inference produces probabilistic outputs, heatmaps, bounding boxes, or dynamic UI reconfiguration, you&#8217;ll need a panel with GPU-accelerated rendering and a high-bandwidth interface (MIPI DSI or LVDS). Standard HMI panels with SPI interfaces and basic display controllers will bottleneck on the rendering pipeline.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q2-how-do-i-choose-between-running-inference-on-the-edge-device-vs-in-the-cloud\">Q2: How do I choose between running inference on the edge device vs. in the cloud?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Edge inference (on-device) eliminates network latency and works without connectivity, but is constrained by the edge processor&#8217;s compute capability. Cloud inference can run larger, more accurate models but introduces 50\u2013200 ms of network round-trip latency. For safety-critical display updates (alarms, robot status), edge inference is strongly preferred because the display pipeline already adds 11\u201337 ms \u2014 adding 50\u2013200 ms of cloud latency on top of that creates an unacceptable total. For non-critical analytics and trend visualization, cloud inference is fine.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q3-what-s-the-minimum-display-size-for-an-always-on-status-indicator\">Q3: What&#8217;s the minimum display size for an always-on status indicator?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For a simple icon + numeric readout (e.g., heartbeat symbol + &#8220;72 BPM&#8221;), 1.0\u20131.5-inch OLED is sufficient. For an alert bar with text (e.g., &#8220;BEARING 3: HIGH VIBRATION&#8221;), 2.0\u20132.4-inch is the minimum for legible text at arm&#8217;s length. QIHAN&#8217;s smallest always-on-capable OLED module is 0.96-inch (128\u00d764), suitable for icon-only status indication.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q4-can-qihan-provide-the-embedded-computing-module-as-well-as-the-display\">Q4: Can QIHAN provide the embedded computing module as well as the display?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. For AIoT applications, we offer integrated solutions that include the display panel, touch panel, optical bonding, embedded computing module (Rockchip or NXP platforms), and interface conversion as needed. We handle the BSP-level display driver integration. Your team handles the application-layer software and AI model deployment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q5-how-do-i-test-the-end-to-end-latency-of-my-display-pipeline\">Q5: How do I test the end-to-end latency of my display pipeline?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The most practical method: connect a high-speed camera (240+ fps) pointed at both the display and a GPIO-triggered LED. When the inference engine produces a result, it toggles the LED and writes the result to the display. The camera captures both events. Count the frames between LED toggle and display update \u2014 each frame at 240 fps = 4.17 ms. This gives you an empirical end-to-end latency measurement without instrumenting the software pipeline.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q6-what-thermal-solutions-do-you-recommend-for-multi-display-aiot-systems-in-sealed-enclosures\">Q6: What thermal solutions do you recommend for multi-display AIoT systems in sealed enclosures?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In order of increasing cost and complexity: (1) thermally conductive gap pads between backlight PCBs and enclosure walls (passive, ~$2\u20135 BOM), (2) aluminum enclosure with integrated heatsink fins (passive, adds enclosure cost), (3) Peltier thermoelectric cooling module with external heatsink (active, ~$15\u201325 BOM, adds 2\u20135W power draw), (4) forced air cooling with filtered intake\/exhaust (active, compromises IP rating). For IP65+ sealed enclosures with &gt;10W internal dissipation, options 2 or 3 are typically required.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"q7-does-qihan-offer-partial-refresh-capable-displays\">Q7: Does QIHAN offer partial-refresh capable displays?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. Several of our TFT modules support partial refresh in command mode via MIPI DSI, allowing only changed regions of the display to be updated. This can reduce display power consumption by 30\u201350% in always-on scenarios where most of the screen content is static. Contact our engineering team with your specific always-on requirements \u2014 the right approach depends on what information needs to remain continuously visible.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"about-qihan\">\u00dcber QIHAN<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/qihantech.com\/de\/about-us\/\" data-type=\"page\" data-id=\"9\">Shanghai Qihan Technology Co., Ltd. (QIHAN)<\/a> is a manufacturer and supplier of custom small and medium-sized display solutions, specializing in TFT LCD, OLED, LCM, touch screen displays, and customized display modules for industrial, medical, automotive, smart home, and consumer electronics applications. With 15+ years of R&amp;D experience and a daily production capacity of 30,000 LCMs, QIHAN provides OEM\/ODM services from concept to mass production.<\/p>","protected":false},"excerpt":{"rendered":"<p>Key Takeaways Table of Contents The term &#8220;AIoT&#8221; \u2014 the convergence of artificial intelligence and the Internet of Things \u2014 has been a trade show buzzword for years. At electronicAsia 2025 in Hong Kong, our team demonstrated a prototype AIoT mainboard driving multiple display outputs with on-device neural network inference. The conversations that followed \u2014 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1395,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","footnotes":""},"categories":[26],"tags":[],"class_list":["post-1382","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lcd-display-news"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/posts\/1382","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/comments?post=1382"}],"version-history":[{"count":2,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/posts\/1382\/revisions"}],"predecessor-version":[{"id":1396,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/posts\/1382\/revisions\/1396"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/media\/1395"}],"wp:attachment":[{"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/media?parent=1382"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/categories?post=1382"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/qihantech.com\/de\/wp-json\/wp\/v2\/tags?post=1382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}