BKWai- turning big data into advanced engineering intelligence

资产监控越来越多地基于真实数据，这有助于使资产更安全并优化其性能。例如，昆斯费里大桥使用来自1000多个传感器的数据来实时监测天气和运动，并在出现问题之前识别出性能问题。数据驱动的建筑环境在整个资产生命周期（从规划到资产建设和预防性维护）中提供重要价值，只有在充分利用所有可用数据源并应用正确的智能时才能充分利用。目前，基于训练有素的工程师对生成数据的手动解释，甚至在地面运动监测或数字孪生应用等监测技术的支持下，虽然增加了显著的价值，但仍然无法提供给定资产的完整图像。BKWai寻求解决的正是这种已知的数据使用方面的市场差距。结合大数据和深度学习的新兴创新，BKWai的目标是开发首个解决方案，该解决方案将能够从大量现场传感器网络、卫星图像和基础设施资产监控的第三方数据中自动提取大规模数据集的关键见解，从而在建筑环境中提供实时工程智能。

Asset monitoring is increasingly based on real-world data, helping to make assets safer and optimise their performance. The Queensferry Crossing bridge, for example, uses data from over 1,000 sensors to monitor weather and motion in real time and identify performance issues before they become problems. A data-driven built environment whilst delivering significant value across an asset's life-cycle -- from planning through to asset construction and preventative maintenance, can only be fully exploited if the full range of data sources available are utilised and the correct intelligence applied. Based on the current manual interpretation of generated data by trained engineers or even when supported by monitoring technologies such as ground movement monitoring or digital twin applications, whilst added significant value, still fail to provide a complete picture of a given asset.It is this known market gap in data use that BKWai seek to address. Combining emerging innovations in big data and deep learning, BKWai aim to develop the first solution that will enable the automatic extraction of key insights from large scale datasets derived from the abundance of on-site sensor networks, satellite imagery and third-party data for infrastructure asset monitoring to provide real time engineering intelligence within the built environment.