跳至正文

Physical AI 决策工具

资源库

面向管理者的现场笔记、评估框架与模板,帮助将工业 AI 试点转化为可部署、可运营、可信赖的产品。

Learning Tracks

Four decision-led paths through the category, commercial opportunity, productization and transitional product leadership.

基于来源的分析

面向物理 AI 的欧盟 AI 法案

适用日期取决于系统的分类方式。请先阅读最新分析,再梳理预期用途、系统边界和义务,然后判断哪个日期才是您的截止日期。

全部资源
全部分类

全部资源

19

  • 模板

    战略

    Worked Product System Evidence Pack

    Inspect a fictional cobot decision, evidence gaps and linked Product System exports. Modeled inputs and accountable roles remain distinct from recorded human approval.

  • 模板

    战略

    Physical AI Lifecycle Economics Stress Test

    Compare a fictional baseline with five declared stresses using the existing simulator. Inspect capital, support, recovery and payback assumptions; these are modeled scenarios, not forecasts.

  • 清单

    战略

    Intralogistics Procurement Evidence Checklist

    Sixteen evidence requests before a fleet commitment, with accountable roles, acceptance questions and hold conditions. Every request starts unassessed; no supplier score or approval is implied.

  • 指南

    战略

    Physical AI Product Management 2027: Research Protocol

    The proposed study question, exploratory design, sample targets, consent boundaries and pre-launch gates. Recruitment has not started and no findings exist.

  • 指南

    技术

    Deploying Mistral On-Prem for Manufacturing — Sovereign, Air-Gapped AI

    How manufacturers deploy Mistral AI on-premise and in air-gapped environments. Covers the Sovereign Model Ladder, Mistral Forge/Studio/Compute stack, use cases across aerospace, automotive, semiconductors, and energy, and EU AI Act compliance.

  • 指南

    技术

    Mistral vs OpenAI vs Anthropic for Industrial & Sovereign AI

    Honest side-by-side comparison of Mistral, OpenAI GPT, and Anthropic Claude for industrial and sovereign AI workloads. Covers data residency, on-prem deployment, fine-tunability, cost at scale, capability ceiling, vendor lock-in, and EU AI Act fit — plus when frontier models genuinely win.

  • 指南

    技术

    Predictive Maintenance AI for Industrial Equipment: Condition Monitoring, RUL & ROI

    How to build predictive maintenance for production equipment: the data foundation (vibration, thermal, and motor-current signatures over OPC-UA and time-series), modeling approaches (anomaly detection, remaining-useful-life, survival models), edge vs cloud inference, CMMS/SCADA integration, and how to quantify ROI (downtime avoided, MTBF). Framed against ISO 13374 condition monitoring and IEC 62443 OT security. Ties to the live CSV-maintenance demo on this site.

  • 指南

    标准

    AI Visual Quality Inspection for Manufacturing: Computer-Vision Defect Detection

    How to deploy computer vision quality inspection on the production line: surface defects, assembly/completeness, and weld inspection — sensor and lighting setup, dataset and annotation strategy, and edge deployment. Includes the honesty boundary: a vision model surfaces candidate indications mapped to a vocabulary like ISO 5817 weld imperfections — it does not assign a certified grade (that needs metrology and your WPS). Ties to the live plant-audit and defect demos on this site.

  • 指南

    技术

    Sim-to-Real for Industrial Robotics: Evidence Before Release

    A product-decision guide to simulation assumptions, transfer tests, target hardware, policy authority, independent checks, recovery and field evidence. Compares simulator and policy choices without treating simulation performance as release proof.

  • 指南

    安全

    Edge AI Under Functional Safety: Deploying AI in ISO 26262 & IEC 62443 Systems

    Deploying AI in safety-critical embedded systems: ISO 26262 ASIL levels, SOTIF (ISO 21448), IEC 61508 SIL, IEC 62443 OT cybersecurity, runtime assurance monitors, operational design domains, and the edge inference toolchain (ONNX/TensorRT).

  • 指南

    技术

    Digital Twin ROI Playbook: From OPC-UA to a Production Twin

    A practical ROI framework for industrial digital-twin programmes: the data foundation (PLC → OPC-UA → time-series → twin), the five-rung maturity ladder, where AI enters (anomaly detection, predictive maintenance, process optimization), how to quantify ROI, and build-vs-buy guidance for manufacturing leaders.

  • 指南

    标准

    AI for Aerospace & Defence-Adjacent Manufacturing: A Civil-First, Dual-Use Primer

    How MRO operators, avionics suppliers, and UAV makers deploy AI for predictive maintenance, automated NDT, and documentation copilots — with a clear-eyed view of DO-178C / DO-254 / ARP4754A certification, EASA's learning-assurance roadmap, and the sovereign on-prem case. Civil-first; no defence contracts or clearances.

  • 指南

    标准

    Auralink: A Physical AI Research Reference Architecture

    A Hyperion-authored Physical AI research reference for EV-charging infrastructure, evaluated in simulation and bounded environments—not a live deployment or client-outcome claim.

  • 指南

    战略

    Physical AI Product Teardowns

    Three shipped physical-AI product families read for the decisions behind them — EV charging reliability, Android Automotive inside a homologated vehicle, and video platforms at 100M+ scale. Public observation, not client case studies.

  • 指南

    战略

    The Prototype-to-Product Decision Gate

    Five decisions that determine whether a Physical AI prototype becomes a product — degraded-mode ownership, variant count, field survival, the scale delta, and who signs the compliance case. Each gate carries the test that answers it and the failure it prevents.

  • 指南

    战略

    Physical AI Product Management: The Complete Guide

    The operating reference for AI products that perceive, decide and act: strategy, discovery, productization, evidence, operating envelopes, safety, platforms, launch, field operations, metrics and scale.

  • 指南

    战略

    Physical AI Product Manager: Role Charter & Hiring Scorecard

    A practical role definition for Physical AI product leaders: decision accountabilities, specialist boundaries, seniority ladder, hiring scorecard, interview loop, first-90-days plan and copyable role brief.

  • 模板

    安全

    留邮箱免费获取

    Safety-Case Evidence Template (ISO 26262 / DO-178C / IEC 61508)

    A practitioner template for structuring an AI safety case: the Claims–Arguments–Evidence skeleton, a HARA input sheet, ASIL/DAL/SIL decomposition placeholders, and a V&V traceability matrix — mapped to ISO 26262, DO-178C and IEC 61508. It is a template and checklist; a notified/certification body assigns the actual rating, we engineer the evidence.

  • 模板

    标准

    留邮箱免费获取

    Edge AI Vendor Evaluation Matrix

    A vendor-neutral scoring matrix for evaluating edge-AI inference hardware and vendors: TOPS/W, P99 latency, ONNX/TensorRT-class format support, on-prem/sovereign deployment, toolchain maturity, functional-safety support, power/thermal envelope, and long-term support — with a weighted 1–5 rubric. Criteria and a scoring method, not a ranked vendor list.

资源止步之处,正是决策开始之时。

描述产品、被卡住的决策、决策期限、高管发起人以及目前可用的证据。Mohammed 会告诉你 Hyperion 是否是合适的委托——以及何时不是。

AI 资源 — 免费指南与模板