用AI与区块链打造农村智慧水务，实现数据可信、实时监控和智能预警。
Smart Water Security with AI and Blockchain-Enhanced Digital Twins
- 基于LoRaWAN与LSTM+孤立森林检测异常数据，提升采集可靠性。
- 区块链上运行的数字孪生系统支持漏水检测与用水预测，延迟低于2秒。
- 适合农村水务管理、智慧城市基础设施建设等场景使用。
农村供水系统面临实时监测缺失、易受网络攻击及数据处理不可靠等问题。本文提出融合LoRaWAN数据采集、机器学习驱动的入侵检测系统（IDS）与区块链增强型数字孪生（BC-DT）平台的集成框架。IDS通过长短期记忆自编码器（LSTM Autoencoder）与孤立森林算法过滤异常或伪造数据，经验证的数据通过私有以太坊区块链上的智能合约记录，采用权威证明（PoA）共识机制。验证后的数据输入实时数字孪生模型，支持漏损检测、用水量预测与预测性维护。实验表明，系统在1000个智能水表规模下可实现每秒超80笔交易（TPS），延迟低于2秒，兼具成本效益与可扩展性。本工作为欠连接农村地区提供了可落地的去中心化供水基础设施安全架构。
原文摘要 · Abstract (English)
Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.
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