F1车队如何玩转大数据

奥斯丁是美国唯一的F1赛事冠军赛大本营，在该城市美洲赛道的后勤维修站，F1赛事粉丝人头攒动。随着阵风划过沥青跑道，一些人握紧了雨披，面部紧绷。其他人则在为自己和赛车拍照，并希望见到自己最喜爱的车手。当某位车手最终现身时，人群立刻涌上前去，向其索要签名和合影，而赛车则暂时被抛之脑后。

然而，这些赛车每一辆都价值900多万美元，仅方向盘的造价就达到了约7.7万美元。这些造价高昂的精密设备不仅能够以时速约320公里的速度在跑道上飞驰，同时也是智能设备。这得益于赛车所携带的数十个传感器，每个传感器都会与跑道、后勤维修人员、现场播报人员和位于欧洲本土的第二工程师团队进行通讯。

The Pit Row at the Circuit of the Americas in Austin, home to the only Formula 1 championship race in the U.S., is packed with fans. Some people clutch their ponchos and wince as gusts whip along the asphalt. Others snap photos of themselves with the cars and hope for a glimpse of their favorite driver. When one finally makes an appearance, a scrum surrounds him, asking for autographs and selfies. The vehicles are momentarily forgotten.

But these machines, each valued at more than $9 million (a steering wheel alone is worth $77,000 or so) are more than just pricey contraptions capable of whizzing around the track at more than 200 miles per hour. They are also intelligent, thanks to the many dozens of sensors fastened to them. Each sensor communicates with the track, the crew in the pit, a broadcast crew on-site, and a second team of engineers back home in Europe.

大部分F1观众认为，赛事的胜负取决于赛车在大奖赛赛道中坡道和急转弯道的表现，但很少有人会意识到，这一赛事还是全球高性能互联计算机之间的角逐。

F1赛事是一个所谓的高风险物联网案例，在赛事期间，各个团队都利用了从实体目标中获取的大量实时数据。体育赛事对这类数据的使用方法尤为先进，甚至一些团队还向其他行业输出这类专长，在这些行业中，瞬息之间完成的海量信息分析往往关乎他人的性命。例如，英国汽车生产商迈凯伦正在向康菲分享其数据系统专长，以便后者将其运用到自身油井设备当中。

“在比赛中，我们会测量需要管理的一切事物，然后进行建模，从而对赛车今后的表现进行智能预测。”迈凯伦应用技术首席创新官杰夫•麦格拉斯说。

迈凯伦团队以历史数据和实况模拟（利用当前赛季传感器获取的数据）为依托，并按照各条跑道的特性来制造赛车。公司利用3D打印机来制作原型部件，然后在风洞中加以测试。通过测试的车身设计将用碳纤维进行构造。这一流程确保了汽车设计的每一个环节都能够用数据来说话。

传感器安装在赛车的底盘和轮胎中以及引擎的各个部位。它们将测量空气对车头的压力和下向力、刹车温度、轮胎压力，甚至赛车在弯道时是在滑动还是主动转向。安装在悬挂系统的传感器测量的是赛车的速度以及受力对赛车的影响。那价值7.7万美元的方向盘呢？它装有能够完成所有动作的旋钮、按键和踏板，车手只需通过按键便能完成从减速一直到向车手头盔中注射液体的动作。

然而，车手对赛车传感器提供的数据却是知之甚少。英菲尼迪红牛车队技术合作方负责人阿兰•匹斯兰德说：“我们尽量不拿数据去干扰车手，车手在驾车时会将自己的感知能力提升到极限。”

F1赛事对于获准进入跑道的团队人员数量有着严格的规定。例如，英菲尼迪红牛车队在赛事现场拥有60名工程师，还有30名在英格兰。匹斯兰德表示，将数据从距离最远的澳大利亚跑道传至车队英国团队所需的时间不到300毫秒。该团队将藉此进行实况模拟，以确定竞赛各个环节的策略，包括换胎时间以及超车的时机。他说：“车手基本上不会再靠直觉来做决定。”

然而，数据分析并没有解决所有的问题。目前，人们仍无法精确地感知车身外侧是否在跑道上，也无法确定轮胎对路面的抓地能力。只有车手才能回答上述问题。麦格拉斯说：“车手仍是我们所能拥有的最好的传感器。”（财富中文网）

译者：冯丰

校对：詹妮

Most Formula 1 spectators expect the race to be won or lost in the hills and hairpins of a Grand Prix circuit. What few realize is that it’s also playing out in powerful, interconnected computers around the world.

A Formula 1 race is a high-stakes example of the so-called Internet of things, where teams tap tremendous amounts of real-time data culled from physical objects. The sport’s use of such information is so sophisticated that some teams are exporting their knowledge to other industries where analyzing enormous amounts of information in the blink of an eye can mean the difference between life and death. For example, British automaker McLaren is sharing its data systems expertise with ConocoPhillips for use on oil rigs.

“We measure whatever we need to manage during the race, and then we model to get the predictive intelligence on how the cars are going to perform,” says Geoff McGrath, chief innovation officer at McLaren Applied Technologies.

The McLaren team builds its race cars for each track based on historical data and simulations generated by the current season’s sensor data. It builds prototype parts with 3D printers and tests them in wind tunnels. Approved designs are constructed in carbon fiber. The process ensures that every aspect of the car’s design is based on data.

Sensors are installed along a car’s chassis, in its tires, and throughout the engine. They measure the stress and downward force of the air on the car’s nose, brake temperature, tire pressure, and even whether the car is sliding or actively turning a corner. Sensors attached to the suspension measure the car’s speed as well as how force affects the vehicle. And that $77,000 steering wheel? It’s packed with knobs, buttons, and pedals that can do everything from slow down the car to deliver a shot of liquid to the driver through his helmet at the press of a button.

Yet for all the data generated by the car, the driver sees little of it. “We keep it to a minimum,” says Alan Peasland, head of technical partnerships at Infiniti Red Bull Racing. “They are maxed out on their cognitive capacity driving these cars.”

Formula 1 has strict rules about the number of team personnel allowed at the track. Infiniti Red Bull, for example, has 60 engineers on-site and 30 in England. It takes less than 300 milliseconds for the data from the farthest track in Australia to reach Infiniti Red Bull’s U.K. team, Peasland says, which runs simulations to determine race strategy for everything from a tire change to an attempt to overtake another driver. “Gut-feel decisions just aren’t made,” he says.

Data analytics haven’t solved everything. There’s still no way to get an accurate sense of where the cars are laterally on the track, and it’s impossible to determine how well a tire is gripping the roadway. The only person who can tell that is the driver. Says McGrath: “The driver is still the best sensor we have.”