BKWai- turning big data into advanced engineering intelligence

资产监控越来越多地基于真实世界的数据，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控天气和运动，并在性能问题成为问题之前识别出来。数据驱动的构建环境在整个资产生命周期--从规划到资产构建和预防性维护--提供显著价值的同时，只有充分利用所有可用的数据源并应用正确的情报，才能得到充分利用。基于目前由训练有素的工程师对生成的数据进行的手动解释，或者即使有地面移动监测或数字孪生应用程序等监测技术的支持，虽然增加了显著的价值，但仍无法提供给定资产的完整图景。正是这种已知的数据使用市场缺口，BKWAI寻求解决。结合大数据和深度学习领域的新兴创新，BKWAI的目标是开发第一个解决方案，该解决方案将能够从来自大量现场传感器网络、卫星图像和第三方数据的大规模数据集中自动提取关键见解，用于基础设施资产监控，以在建筑环境中提供实时工程情报。

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.