Metadata Filter¶
The flir-filter-metadata filter allows data to be added to assets within FLIR Bridge. Metadata takes the form of fixed data points that are added to an asset used to add context to the data. Examples of metadata might be unit of measurement information, location information or identifiers for the piece of equipment to which the measurement relates.
A metadata filter may be added to either a south service or a north task. In a south service it will be adding data for just those assets that originate in that service, in which case it probably relates to a single machine that is being monitored and would add metadata related to that machine. In a north task it causes metadata to be added to all assets that the FLIR Bridge is sending to the up stream system, in which case the metadata would probably related to that particular FLIR Bridge instance. Adding metadata in the north is particularly useful when a hierarchy of FLIR Bridge systems is used and an audit trail is required with the data or the individual FLIR Bridge systems related to some physical location information such s building, floor and/or site.
To add a metadata filter
Click on the Applications add icon for your service or task.
Select the metadata plugin from the list of available plugins.
Name your metadata filter.
Click Next and you will be presented with the following configuration page
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Enter your metadata in the JSON array shown. You may add multiple items in a single filter by separating them with commas. Each item takes the format of a JSON key/value pair and will be added as data points within the asset.
Enable the filter and click on Done to activate it
Example Metadata¶
Assume we are reading the temperature of air entering a paint booth. We might want to add the location of the paint booth, the booth number, the location of the sensor in the booth and the unit of measurement. We would add the following configuration value
{
"value": {
"floor": "Third",
"booth": 1,
"units": "C",
"location": "AirIntake"
}
}
In above example the filter would add “floor”, “booth”, “units” and “location” data points to all the readings processed by it. Given an input to the filter of
{ "temperature" : 23.4 }
The resultant reading that would be passed onward would become
{ "temperature" : 23.5, "booth" : 1, "units" : "C", "floor" : "Third", "location" : "AirIntake" }
This is an example of how metadata might be added in a south service. Turning to the north now, assume we have a configuration whereby we have several sites in an organization and each site has several building. We want to monitor data about the buildings and install a FLIR Bridge instance in each building to collect building data. We also install a FLIR Bridge instance in each site to collect the data from each individual FLIR Bridge instance per building, this allows us to then send the site data to the head office without having to allow each building FLIR Bridge to have access to the corporate network. Only the site FLIR Bridge needs that access. We want to label the data to say which building it came from and also which site. We can do this by adding metadata at each stage.
To the north task of a building FLIR Bridge, for example the “Pearson” building, we add the following metadata
{
"value" : {
"building": "Pearson"
}
}
Likewise to the “Lawrence” building FLIR Bridge instance we add the following to the north task
{
"value" : {
"building": "Lawrence"
}
}
These buildings are both in the “London” site and will send their data to the site FLIR Bridge instance. In this instance we have a north task that sends the data to the corporate headquarters, in this north task we add
{
"value" : {
"site": "London"
}
}
If we assume we measure the power flow into each building in terms of current, and for the Pearson building we have a value of 117A at 11:02:15 and for the Lawrence building we have a value of 71.4A at 11:02:23, when the data is received at the corporate system we would see readings of
{ "current" : 117, "site" : "London", "building" : "Pearson" }
{ "current" : 71.4, "site" : "London", "building" : "Lawrence" }
By adding the data like this it gives as more flexibility, if for example we want to change the way site names are reported, or we acquire a second site in London, we only have to change the metadata in one place.
See Also¶
flir-filter-asset - A FLIR Bridge processing filter that is used to block or allow certain assets to pass onwards in the data stream
flir-filter-conditional-labeling - Attach labels the reading data based on a set of expressions matched against the data stream.
flir-filter-ednahint - A hint filter for controlling how data is written using the eDNA north plugin to AVEVA’s eDNA historian
flir-filter-enumeration - A filter to map between symbolic names and numeric values in a datapoint.
flir-filter-expression - A FLIR Bridge processing filter plugin that applies a user define formula to the data as it passes through the filter
flir-filter-fft - A FLIR Bridge processing filter plugin that calculates a Fast Fourier Transform across sensor data
flir-filter-normalise - Normalise the timestamps of all readings that pass through the filter. This allows data collected at different rate or with skewed timestamps to be directly compared.
flir-filter-omfhint - A filter plugin that allows data to be added to assets that will provide extra information to the OMF north plugin.
flir-filter-python35 - A FLIR Bridge processing filter that allows Python 3 code to be run on each sensor value.
flir-filter-rename - A FLIR Bridge processing filter that is used to modify the name of an asset, datapoint or both.
flir-filter-rms - A FLIR Bridge processing filter plugin that calculates RMS value for sensor data
flir-filter-sam - A single Asset Model filter for creating a semantic model of an asset from one or more data sources
flir-filter-scale-set - A FLIR Bridge processing filter plugin that applies a set of sale factors to the data
flir-filter-statistics - Generic statistics filter for FLIR Bridge data that supports the generation of mean, mode, median, minimum, maximum, standard deviation and variance.