The AMEE data wiki is a useful resource for this.
AMEE数据wiki是实现这个目标的一个有用资源。
The AMEE API consists of two parts: profiles and data.
AMEEAPI包含两个部分：配置文件和数据。
It discussed how the AMEE API works, showed you how and where to store data.
本文讨论了AMEEAPI的工作方式，存储数据的方式和位置。
In this article, we've looked at a variety of ways data can be stored in AMEE.
在本文中，我们探讨了几种在AMEE中保存数据的方法。
The first step towards using the AMEE platform is to sign up for an access key.
使用AMEE平台的第一步是注册以获取访问密匙。
Likewise, the CO2 emission results generated by AMEE have default units of kg/year.
同样，AMEE生成的CO2排放结果的默认单位是kg/year。
As mentioned earlier, AMEE includes built-in support for a wide range of unit conversions.
如前所述，AMEE包括对广泛的单位会话的内置支持。
The "Powered by AMEE" logo shows that your application complies with authoritative standards.
“Poweredby AMEE”标记表示您的应用程序遵守权威标准。
All scripts developed here are available to download from the AMEE source-control system (see Resources).
这里所开发的所有脚本都可从amee源控系统下载(参见参考资料)。
In this article, you learned to use the AMEE API to embed environmental intelligence into your applications.
在本文中，您学习了如何使用AMEEAPI在您的应用程序中嵌入环境智能。
The maximum time resolution of the AMEE platform is 1 minute. If seconds are specified, they are rounded down.
AMEE 平台的最大时间分辨度是1分钟，如果指定了秒数，则会四舍五入。
The choice of which calculation methodology to use is an essential part of getting started with the AMEE platform.
选择计算方法是使用AMEE平台的重要环节。
We need to supply some extra parameters here, as we need to tell AMEE how much energy we are using for each time period.
因为我们需要告诉AMEE每个周期使用的能量值，所以我们还需要一些其他的参数。
AMEE allows you to store time information along with your data, so that you can build up a series of data points over time.
AMEE允许在存储数据的同时存储时间信息，以便根据时间构建一系列数据点。
To obtain a carbon emission value from AMEE, we need to create profile items for each piece of equipment we want to measure.
要从amee获得碳释放量，我们需要为我们想要测量的每个设备创建相应的配置文件项。
You can store thousands of different types of energy use in AMEE by finding the appropriate category and creating the right items.
通过发现适当的类别并创建正确的项，您可以将数千种不同类型的能源使用存储在AMEE中。
After making the connection to AMEE, we perform a drill-down query to get the correct data item for the information we want to store.
在连接到AMEE后，我们执行一个向下钻取的查询来获得针对我们想要存储的信息的正确的数据项。
All unit conversion calculations are automatically handled by AMEE, giving you the correct results for your application with the minimum of effort.
AMEE自动处理所有单位会话计算，只需最小的工作量就能为您的应用程序提供正确的结果。
They are subject to change across different instances of the AMEE platform, so they will be different between the live and stage platforms, for instance.
它们在AMEE平台的不同实例间会发生变化，比如，它们在实时平台和准备平台之间就不同。
When you store your energy data in an AMEE profile, these methodologies are automatically applied to give a reliable carbon footprint for your energy use.
当您将能源数据存储在一个AMEE配置文件时，这些方法将自动应用，为您的能源使用提供一个可靠的碳 “足迹”。
Future articles will go into greater depth on how to create an end-to-end application using AMEE, but until then, sign up, have a go, and happy hacking!
我还将撰写一些文章深入介绍使用AMEE创建一个端对端应用程序，在此之前，请您注册 AMEE 平台并进行一些有益的尝试。
This is because privacy and security of data are of utmost importance; all data stored in AMEE is anonymized, so you cannot retrieve profile data based on personal information.
这是因为数据隐私和数据安全在今天很重要，存储在AMEE中的所有数据都是匿名的，因此不能根据个人信息检索配置文件数据。
If you create two profile items with the same data item UID, and which overlap the same time, then the AMEE API will complain as it can't form a sensible time series from the data.
如果您使用相同的数据项uid创建了两个配置项，而且时间重叠，那么AMEEAPI将会报错，因为它不能从该数据形成一个合理的时间时序。
We can measure CPU usage once a minute, convert the result into a power usage using a defined mathematical model, and submit the result to AMEE for a high-resolution estimate of emissions.
我们可以每一分钟对CPU使用做一次测量，然后用一个定义好的数学模型将每次的测量结果转化为一个电量使用值，并将这个结果提交给AMEE以获得一个更精确的排放量评估值。
We can measure CPU usage once a minute, convert the result into a power usage using a defined mathematical model, and submit the result to AMEE for a high-resolution estimate of emissions.
我们可以每一分钟对CPU使用做一次测量，然后用一个定义好的数学模型将每次的测量结果转化为一个电量使用值，并将这个结果提交给AMEE以获得一个更精确的排放量评估值。
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