Tank is one of the elements of a water network infrastructure. Now in this link, you can generate random examples of a Tank. Refresh the page and you’ll get several examples.
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Category: Smart Water Domain
Drafting service for creation of automatic payloads of a Smart Data Model
We are in the way of creating a service for testing purposes for getting payloads according to a data model.
The service is under debugging, so it cannot be trusted for production purposes. Let us know issues here
In order to access it has to access this URL (https://smartdatamodels.org/extra/payload_generator.php) with these two parameters token and dataModel
I.e. with a weather observation in the link below:
https://smartdatamodels.org/extra/payload_generator.php?token=sSdme8954c9&dataModel=%22WeatherObserved (reload page several times for generation)
In the production stage, there will be necessary a token in order to, if necessary, to limit the access for a single user. Additionally, it should be available the options of normalized and keyvalues and for the NGSI v2 and LD.
Drafting service for checking properties of a new data model
We are in the way of creating a service for those contributors willing to contribute with a complete data model.
The service is under debugging, so it cannot be trusted for production purposes. Let us know issues here
In order to access it has to access this URL (https://smartdatamodels.org/extra/check_properties.php) with this parameter ?urlSchema=[url of the raw access o the JSON schema]
I.e. a complete link could be
It returns a JSON payload indicating if this property is available for use (not used in any other data model) or in the case it is already being used what data models are currently using.
Official list of unitcodes
Whenever there is measurement it could be necessary to use the unitCode for setting the units for the value.
Now the guidelines for the data model’s creation include the place to find out these unit codes. See section units.
The list of UN/CEFACT Common Code (3 characters) can be download from this page. Or the list directly from here.
Feature/add device placement iot for water modelling team.
Included into the data model a first version for the placement of device. This feature allow a better use of the device data model in the smart sensoring domain .
See the Pull request.
Automatic creation of examples
In order to reduce the amount of work in the contribution of data models, we have a script that generates the key-values format of a normalized NGSI LD payload.
It has been operated on 46 data models currently in the repository.
In a close future instead of submitting 4 examples, it will be only necessary to include 2 examples, the normalized payloads for NGSI v2 and NGSI LD (the other two would be generated automatically).
Making examples more consistent
All the repositories have been normalized in the naming of the core examples. So:
- Key values in NGSI v2 is named example.json
- Key values in NGSI LD is named example.jsonld
- Normalized (default) in NGSI v2 is named example-normalized.json
- Normalized (default) in NGSI LD is named example-normalized.jsonld
It is possible to have more examples in the directory. And in the same directory of every data model, there are other exports (csv) for the use of the data model users.
The goal is that this 4 examples were present in every data model
Automated README for Subjects
As promised the README.md at Subject level explaining the different data models is automated, and therefore there is no need to be contributed by the contributors. See an example here for the weather subject
Thus, the README.md is based on the data models and their descriptions, linking to the current contributors
Soon the README.md for the domain will be also automated.
Best
Automated README.md for data models
Currently, the README.md at data models level (inside every directory in a Subject) explaining the contents of the data models is automated, and therefore there is no need to be contributed by the contributors. See an example here for the weather forecast.
Thus, the README.md is based on the examples provided, the specification and the model.yaml.
Soon the README.md for the Subject will be also automated.
Incubated data models
if you are browsing the repository you can realise that now there are several data models ended with the term ‘_incubated’.
This suffix denotes that there will be ‘soon’ a new data model.
The readme in the folder points to the repository where this data model is being created.
Once finished (all the docs, examples, etc) the _incubated suffix will be removed and the data model will be fully included into the data models.
some examples:
BuildingType_incubated(published 30-11-21)
PhreaticObserved_incubated (published 31-5-2021)
RainFallRadarObserved_incubated (published 10-5-2021)
etc
Slack channel
Validate a payload with a data model