PFI TT: Intelligent quality assurance and integration tool for sewer inspection data

“创新伙伴-技术转化”计划的更广泛影响及商业潜力，是监察污水渠的质素，以达致明显的公众利益。当前处理数据不兼容和质量问题的做法是手动的，而且耗时。此外，这些实践是为单个项目定制的，这个过程很难适应不同的项目。此外，由于缺乏适当的数据管理解决方案，下水道检查数据在公用事业公司内部仍然是碎片化的，这使得资产管理无法访问数据。设想中的技术将有助于将下水道检查数据与标准的PACP（管道评估认证计划）格式相结合，确保数据质量，并集成历史数据，以便轻松安全地访问，从而可以根据实际情况制定具有成本效益的资产管理计划。每个实用程序都可以节省高达80%的时间（大约几个月）用于手动数据质量保证。有了准确和可访问的下水道状况数据，预计整个行业每年至少可以节省20%（约6亿美元）的管道修复支出。更重要的是，下水道基础设施条件的改善可以大大减少卫生下水道溢流事件引起的胃肠道疾病急诊室就诊。建议的项目是：1)开发一种人工智能（AI）算法来解决数据不兼容问题，以保持数据保真度，并促进不同软件平台之间的数据交换；2)建立一个全面的人工智能方法框架，以自动检测和处理数据质量问题，包括由于管道缺陷或人为错误而导致的检测操作中断所导致的数据记录不一致、不完整和重复；3)使用区块链技术规范市政公用事业在管理下水道状况数据方面的做法。该PFI-TT项目将定制新兴的人工智能/机器学习和区块链技术，以开发高效的数据格式转换算法、有效的数据质量保证框架和支持区块链的下水道数据管理工具，目的是节省工程师在数据处理方面的工作量，并使他们有更多的时间进行工程决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is conduct quality monitoring of sewers for clear public benefit. Current practices in handling data incompatibility and quality issues are manual and time-consuming. In addition, these practices are customized for individual projects, a process hard to adapt to different projects. Moreover, the sewer inspection data remains fragmented within the utilities due to the lack of proper data management solutions, which makes data less accessible for asset management. The envisioned technology will help to combine sewer inspection data with standard PACP (Pipeline Assessment Certification Program) format, ensure data quality, and integrate historical data for easy and secured access so that cost-effective asset management plans can be developed based on true conditions. Each utility could save up to 80% of the time (on the order of months) for manual data quality assurance. With accurate and accessible sewer condition data, it is anticipated that at least 20% (approximately $600 million) of the annual spending on pipe rehabilitation can be saved for the entire industry. More importantly, improved sewer infrastructure condition can greatly reduce gastrointestinal (GI) illness emergency room visits induced by sanitary sewer overflow events. The proposed project is to 1) develop an artificial intelligence (AI)-enabled algorithm to resolve data incompatibility issues to maintain data fidelity and facilitate data exchange among different software platforms; 2) establish a holistic AI-enabled methodological framework to automatically detect and treat data quality issues, including data records inconsistency, incompleteness, and duplicates, caused by the interruption of inspection operations due to pipe defects or human errors; and 3) standardize municipal utilities’ practices in managing sewer condition data using blockchain-enabled technologies. This PFI-TT project will tailor the emerging AI/machine learning and blockchain technologies to develop an efficient data format translation algorithm, effective data quality assurance framework, and a blockchain-enabled sewer data management tool, with the objective to save engineers’ effort in data processing and render them more time for engineering decision making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.