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数字水务进展以及从汽车和飞机工业自动化发展中汲取的经验教训
Digital Water Developments and Lessons Learned from Automation in the Car and Aircraft Industries
提供水务服务和涉水卫生服务是一项全球性关键挑战。水务设施服务和废水设施服务具有规模性、复杂性和关键性等特点，使其在规划和管理方面变得极为困难。数字化革命已深入人类生活的许多领域，水务行业也开始从数字化转型中受益。有效利用气象遥感和土壤湿度数据可以提高灌溉效率（即粮食产量），利用人工智能可以更好地检测管道网络的异常和故障，利用自然启发式优化方法可以改善系统的管理和规划，以及数字孪生和机器人技术的频繁利用，所有这些都展现了数字化技术在改善复杂水务系统管理上的巨大潜力。但是，这些技术也有附加风险，如网络安全风险、不正当使用、过度信任数字解决方案与自动化的性能和准确度等。本文明确了水务行业数字化技术的主要进展，并将取证工程原则应用到一些在自动化和数字化转型较为领先的领域，对其故障事件进行分析。新型数字化技术失误是引发汽车和飞机行业重大事故的潜在原因（如特斯拉自动驾驶汽车和波音737 MAX飞机），确定失误原因并将经验应用于水务行业的类似风险识别中，吸取教训，避免失败。重要发现表明：①自动化要求在回路系统配置人员；②人工操作者必须充分掌握技术，并接受技术培训；③在发生技术故障时，应当采取应急式人工干预；④虽然冗余传感器会增加成本，但能减少由传感器读数失误带来的风险；⑤必须考虑网络安全风险；⑥鉴于水务系统的自动化和互联性不断增强，必须考虑相关伦理问题。这些发现也指出了与水务行业数字化转型相关的主要研究领域。
The provision of water and sanitation services is a key challenge worldwide. The size, complexity, and critical nature of the water and wastewater infrastructure providing such services make the planning and management of these systems extremely difficult. Following the digital revolution in many areas of our lives, the water sector has begun to benefit from digital transformation. Effective utilization of remotely sensed weather and soil moisture data for more efficient irrigation (i.e., for food production), better detection of anomalies and faults in pipe networks using artificial intelligence, the use of nature-inspired optimization to improve the management and planning of systems, and greater use of digital twins and robotics all exhibit great potential to change and improve the ways in which complex water systems are managed. However, there are additional risks associated with these developments, including—but not limited to—cybersecurity, incorrect use, and overconfidence in the capability and accuracy of digital solutions and automation. This paper identifies key advances in digital technology that have found application in the water sector, and applies forensic engineering principles to failures that have been experienced in industries further ahead with automation and digital transformation. By identifying what went wrong with new digital technologies that might have contributed to high-profile accidents in the car and aircraft industries (e.g., Tesla self-driving cars and the Boeing 737 Max), it is possible to identify similar risks in the water sector, learn from them, and prevent future failures. The key findings show that: ① Automation will require “humans in the loop”; ② human operators must be fully aware of the technology and trained to use it; ③ fallback manual intervention should be available in case of technology malfunctioning; ④ while redundant sensors may be costly, they reduce the risks due to erroneous sensor readings; ⑤ cybersecurity risks must be considered; and ⑥ ethics issues have to be considered, given the increasing automation and interconnectedness of water systems. These findings also point to major research areas related to digital transformation in the water sector.
数字化 / 自动化 / 水行行业 / 潜在风险 / 经验教训
Digitalization / Automation / Water Sector / Potential Risks / Lessons
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