Applying AI to planning requires a trove of data, and many power utilities have the operational data necessary to begin the kind of advanced analytics machine learning can provide. National Grid recently announced that it will pilot the machine-learning tools from technology firm Urbint to anticipate safety issues on its construction projects, bringing an AI advisor in to spot problems on jobsites before they lead to safety incidents.

Urbint战略和技术运营副总裁Lindsay Jenkins解释说：“国家电网是我们减少损害技术的早期采用者之一，因此我们将解决方案扩展到工人安全。”URBINT镜头的工人安全镜头检查了公用事业的项目数据，根据现场条件和所涉及的工作类型以及安全事件的相关历史和接近失误的事件进行风险评估。

詹金斯说：“我们的模型的投入与公司的项目计划相关，因此我们知道正在计划的工作并可以识别现场条件。”

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Urbint的模型为不同级别的用户生成可用报告。建筑主管为其项目获得更新的风险评分，并可以使用这些指标来提前计划。“它可以推动他们的现场决定，就像今天一样停止工作日，因为在某些天气中的这些任务容易出现安全事件。也许近距离错过有趋势，需要安全简报。”詹金斯说。Urbint在管理级别提供了不同的仪表板，并在公用事业公司的整个风险投资组合中提供了一项视图，以识别安全趋势。

“This technology is very exciting. It has the potential to pull all this information into one place so we can do some analytics,” says Walter Fromm, vice president of capital delivery for National Grid Gas. “That way, a worker or superintendent out in the field can see before they start what is happening, and take action.”

“We bring our own biases to the jobsite,” says Jenkins. “This is a form of support to take that veil off and use proven methodologies to identify hazards.”

国家电网’s pilot will cover its gas-related construction across the Northeast. Fromm says that if it’s successful, he’d like to roll Urbint out across the utility’s entire portfolio. “I don’t want these safety alerts to be optional,” he says. “Our workers are out there—a lot is going on. My aspiration is this technology will be a control, so they will have those safety conversations in the morning before going to work.”