商品描述
Get the eBook free when you register your print book at Manning. "Makes the theory tangible and the tools accessible, from a single sensor to a city-scale system."
--Anthony Townsend, Cornell University A "digital twin" is a virtual replica of a physical system that allows operations engineers to perform predictive maintenance, run risk-free simulations, and make smart, data-driven decisions. Digital Twins in Action teaches you how to combine methods from software development, data science, robotics, and visual design to create a powerful integrated platform. It uses both standard open sources tools and commercial frameworks. Grounded firmly in practical reality, it clarifies a topic clouded by marketing speak and vague definitions. This hands-on book for software developers and automation engineers guides you step-by-step as you build a small-scale twin you can run in your office or home environment. Author and industry veteran Greg Biegel introduces essential mental models like the "Opportunity-to-Data" Mapping Chain and the Minimum Viable Digital Representation (MVDR) to ensure your design serves concrete goals. Across 10 in-depth chapters, you will learn to prioritize diverse information sources, extract legacy documents using OCR, deploy physical sensor networks over LoRaWAN or Wi-Fi, and organize real-time streams with standardized MQTT topic hierarchies. Reviewer Sanjay Basu, PhD, VP of Gen AI and GPU Infrastructure at Oracle remarked, "Digital Twins in Action takes you from a five-dollar temperature sensor to an AI agent making autonomous decisions!" Because accurate visual simulations are an integral part of a digital twin, this book dives into modeling and visualization techniques that make virtual systems accessible to both humans and machines. You'll master data integration across relational databases, Parquet files, and Delta tables. Then, you'll construct a semantic model of reality using ontologies and knowledge graphs in Memgraph, render responsive 2D dashboards using Grafana, and locate 3D mesh models geospatially on an interactive digital globe using CesiumJS and WebGL. Modern digital twins rely on advanced data analysis, integrating inference and simulation. You will build supervised image classifiers to act as virtual sensors, use unsupervised Isolation Forests for anomaly detection, and apply AutoGluon for time-series forecasting. You'll also explore continuous and discrete event simulations and learn how to construct autonomous AI agents that reason, plan, and optimize physical behaviors. Finally, you will learn to secure the cyber-physical boundary so you can confidently deploy, monitor, and scale a resilient, auditable digital twin. What's inside - Model physical environments using semantic knowledge graphs and ontologies
- How to deploy hybrid IoT sensor networks and stream real-time data
- Integrate predictive machine learning, physics simulations, and AI agents
- The five-level digital twin maturity model About the reader For software developers, data engineers, and architects who know the basics of machine learning. No prior experience in visual design or industrial automation required. About the author Greg Biegel has designed and built digital twins in production for over 20 years. His extensive industry experience includes six years spent developing a state-of-the-art industrial digital twin platform from the ground up for a leading Australian energy producer. Table of Contents 1 Bridging the physical and digital worlds
2 Mapping physical systems to digital representations
3 Sensing the real world
4 Data integration and management
5 Modeling reality
6 2D Visualization and insight
7 Spatial context and 3D insight
8 Integrating inference and intelligence
9 Predicting outcomes with simulation
10 Digital twins in production
A Building a LoRaWAN network
B Building a custom IoT sensor
C Capturing a 3D model using photogrammetry
--Anthony Townsend, Cornell University A "digital twin" is a virtual replica of a physical system that allows operations engineers to perform predictive maintenance, run risk-free simulations, and make smart, data-driven decisions. Digital Twins in Action teaches you how to combine methods from software development, data science, robotics, and visual design to create a powerful integrated platform. It uses both standard open sources tools and commercial frameworks. Grounded firmly in practical reality, it clarifies a topic clouded by marketing speak and vague definitions. This hands-on book for software developers and automation engineers guides you step-by-step as you build a small-scale twin you can run in your office or home environment. Author and industry veteran Greg Biegel introduces essential mental models like the "Opportunity-to-Data" Mapping Chain and the Minimum Viable Digital Representation (MVDR) to ensure your design serves concrete goals. Across 10 in-depth chapters, you will learn to prioritize diverse information sources, extract legacy documents using OCR, deploy physical sensor networks over LoRaWAN or Wi-Fi, and organize real-time streams with standardized MQTT topic hierarchies. Reviewer Sanjay Basu, PhD, VP of Gen AI and GPU Infrastructure at Oracle remarked, "Digital Twins in Action takes you from a five-dollar temperature sensor to an AI agent making autonomous decisions!" Because accurate visual simulations are an integral part of a digital twin, this book dives into modeling and visualization techniques that make virtual systems accessible to both humans and machines. You'll master data integration across relational databases, Parquet files, and Delta tables. Then, you'll construct a semantic model of reality using ontologies and knowledge graphs in Memgraph, render responsive 2D dashboards using Grafana, and locate 3D mesh models geospatially on an interactive digital globe using CesiumJS and WebGL. Modern digital twins rely on advanced data analysis, integrating inference and simulation. You will build supervised image classifiers to act as virtual sensors, use unsupervised Isolation Forests for anomaly detection, and apply AutoGluon for time-series forecasting. You'll also explore continuous and discrete event simulations and learn how to construct autonomous AI agents that reason, plan, and optimize physical behaviors. Finally, you will learn to secure the cyber-physical boundary so you can confidently deploy, monitor, and scale a resilient, auditable digital twin. What's inside - Model physical environments using semantic knowledge graphs and ontologies
- How to deploy hybrid IoT sensor networks and stream real-time data
- Integrate predictive machine learning, physics simulations, and AI agents
- The five-level digital twin maturity model About the reader For software developers, data engineers, and architects who know the basics of machine learning. No prior experience in visual design or industrial automation required. About the author Greg Biegel has designed and built digital twins in production for over 20 years. His extensive industry experience includes six years spent developing a state-of-the-art industrial digital twin platform from the ground up for a leading Australian energy producer. Table of Contents 1 Bridging the physical and digital worlds
2 Mapping physical systems to digital representations
3 Sensing the real world
4 Data integration and management
5 Modeling reality
6 2D Visualization and insight
7 Spatial context and 3D insight
8 Integrating inference and intelligence
9 Predicting outcomes with simulation
10 Digital twins in production
A Building a LoRaWAN network
B Building a custom IoT sensor
C Capturing a 3D model using photogrammetry
商品描述(中文翻譯)
在Manning註冊您的印刷書籍時可免費獲得電子書。
「使理論具體化，工具可及，從單一感測器到城市規模系統。」--安東尼·湯森德，康奈爾大學 「數位雙胞胎」是物理系統的虛擬複製品，允許運營工程師執行預測性維護、進行無風險模擬，並做出智能的數據驅動決策。數位雙胞胎實戰教您如何結合軟體開發、數據科學、機器人技術和視覺設計的方法，創建一個強大的集成平台。它使用標準的開源工具和商業框架。這本書扎根於實際現實，澄清了被市場術語和模糊定義所籠罩的主題。 這本針對軟體開發人員和自動化工程師的實用書籍逐步指導您構建一個可以在辦公室或家庭環境中運行的小型雙胞胎。作者和行業資深人士格雷格·比格爾介紹了「機會到數據」映射鏈和最小可行數位表示（MVDR）等基本思維模型，以確保您的設計服務於具體目標。在10個深入的章節中，您將學會優先考慮多樣的信息來源，使用OCR提取舊有文檔，通過LoRaWAN或Wi-Fi部署物理感測器網絡，並使用標準化的MQTT主題層級組織實時數據流。評審桑賈伊·巴蘇，博士，甲骨文公司Gen AI和GPU基礎設施副總裁評論道：「數位雙胞胎實戰讓您從五美元的溫度感測器走向一個能夠自主決策的AI代理！」 由於準確的視覺模擬是數位雙胞胎的核心部分，本書深入探討了建模和可視化技術，使虛擬系統對人類和機器都可及。您將掌握跨關聯數據庫、Parquet文件和Delta表的數據整合。然後，您將使用本體論和知識圖在Memgraph中構建現實的語義模型，使用Grafana渲染響應式2D儀表板，並使用CesiumJS和WebGL在互動數位地球上地理定位3D網格模型。 現代數位雙胞胎依賴於先進的數據分析，整合推理和模擬。您將構建監督式圖像分類器作為虛擬感測器，使用無監督的Isolation Forest進行異常檢測，並應用AutoGluon進行時間序列預測。您還將探索連續和離散事件模擬，並學習如何構建能夠推理、計劃和優化物理行為的自主AI代理。最後，您將學會如何保護網絡物理邊界，以便自信地部署、監控和擴展一個具有彈性和可審計的數位雙胞胎。 內容概覽 - 使用語義知識圖和本體論建模物理環境
- 如何部署混合IoT感測器網絡並串流實時數據
- 整合預測性機器學習、物理模擬和AI代理
- 五級數位雙胞胎成熟度模型 讀者對象 適合了解機器學習基礎的軟體開發人員、數據工程師和架構師。無需具備視覺設計或工業自動化的先前經驗。 關於作者 格雷格·比格爾在生產中設計和構建數位雙胞胎已有超過20年的經驗。他的廣泛行業經驗包括為一家領先的澳大利亞能源生產商從零開始開發一個最先進的工業數位雙胞胎平台的六年。 目錄 1 橋接物理與數位世界
2 將物理系統映射到數位表示
3 感知真實世界
4 數據整合與管理
5 建模現實
6 2D可視化與洞察
7 空間上下文與3D洞察
8 整合推理與智能
9 使用模擬預測結果
10 生產中的數位雙胞胎
A 建立LoRaWAN網絡
B 建立自定義IoT感測器
C 使用攝影測量法捕捉3D模型
作者簡介
Greg Biegel has been building digital representations of physical systems for large organizations for over 20 years, including 6 years creating a state of the art industrial digital twin platform for a leading Australian energy producer. He holds Computer Science degrees from Rhodes University, South Africa and Trinity College, Dublin.
作者簡介(中文翻譯)
Greg Biegel 在大型組織中建立物理系統的數位表示已超過20年，其中包括為一家領先的澳大利亞能源生產商創建最先進的工業數位雙胞胎平台的6年經驗。他擁有南非羅德大學和都柏林三一學院的計算機科學學位。
