Category: Smart Cities domain

More Zero Emission Buildings data models released

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There are 20 new data models in the subject Zero Emission Buildings belonging to the Smart cities domain

Thanks to the contributors of Mitsubishi electric for the work and for being the pioneers of the new testing process that improves previous one.

    • AirConditionerTerminal. Information on Air conditioner terminal (maker, model, serial number, etc.).

    • Area. Information on Area (Area type, height, space area, etc.).

    • AreaEnvironmentForecast. Information on AreaEnvironmentForecast(Area type, accuracy, simulation process, etc.).

    • Beacon. Information on Beacon (maker, model, serial number, etc.).

    • BuildingZEB. Information on Building of ZEB (Area, cnstruction type, height, etc.).

    • DeviceForecast. Information on DeviceForecast (Accuracy, controlled property, forecast value, etc.).
    • Elevator. Information on Elevator (maker, model, specification, etc.).

    • Equipment. Information on Equipment (maker, model, serial number, etc.).

    • Fan. Information on Fan (maker, model, specification, etc.).

    • GatewayController. Information on Beacon (maker, model, serial number, etc.).

    • IndoorAirConditioner. Information on IndoorAirConditioner (maker, model, specification, etc.).

    • Lighting. Information on Lighting (maker, model, specification, etc.).

    • OutdoorAirConditioner. Information on OutdoorAirConditioner (maker, model, specification, etc.).

    • OutdoorAirTreatingUnit. Information on OutdoorAirTreatingUnit (maker, model, specification, etc.).

    • Room. Information on Room (Area type, height, level, etc.).

    • Sensor. Information on Sensor (maker, model, serial number, etc.).

    • Storey. Information on Storey (Area, height, level, etc.).

    • TotalHeatExchanger. Information on TotalHeatExchanger (maker, model, specification, etc.).

    • WaterHeater. Information on WaterHeater (maker, model, specification, etc.).

Zero emission buildings data models released

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There are several new data models in the new subject Zero Emission Buildings belonging to the Smart cities domain

Thanks to the contributors of Mitsubishi electric for the work and for being the pioneers of the new testing process that improves previous one.

  • Column. Information on a given Column of Building (Shape, thermalTransmission, volumetricSpecificHeat, etc.).

  • Door. Information on a given Door of Building (Shape, thermal transmission, door type, etc.)

  • Glass. Information on a given Glass of Building (Glass name, color, thermal conductivity, etc.)

  • Material. Information on a given Material of Building (Material name, color, thermal conductivity, etc.)

  • MaterialLayer. Information on a given MaterialLayer of Building (Shape, material name, thickness, etc.)

  • Opening. Information on a given Opening of Building (Shape, surface, thickness, etc.)

  • Slab. Information on a given Slab of Building (Shape, thickness, slab type, etc.)

  • Stair. Information on a given Stair of Building (Shape, steps, stair type, etc.)

  • Window. Information on a given Window of Building (Shape, surface glass, thermalTransmission, etc.)

New script for testing several data models at the same time.

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Most of the files of the testing process have been updated and make it available the source code:

https://github.com/smart-data-models/data-models/tree/master/test_data_model

But also there is a new file

multiple_tests.py

This file enables you to test all the data models located in a internal subject (subdirectories of the root one). Currently this option is not available as a form but if you send us an email to our infno@smartdatamodels.org

we could create a specific form for that

See here an example of the outcome.

Another tiny improvement on the new testing process (ngsild payloads)

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In the new testing process, 4th option in the tools menu, now it is available a new test that checks if the example-normalized.jsonld is a valid NGSI LD file.

This process helps contributors to debug their data models before submit them officially (where there will be new tests before final approval)

The source code for the test is available at the repo.

Remember that if you want to improve / create a new test, just create a PR on the repo.

Tiny improvement on the new testing process

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In the new testing process, 4th option in the tools menu, now it is available a new test that checks if the example-normalized.json is a valid NGSIv2 file.

This process helps contributors to debug their data models before submit them officially (where there will be new tests before final approval)

The source code for the test is available at the repo.

Remember that if you want to improve / create a new test, just create a PR on the repo.

Improved test method for data models

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When you want to contribute a new data model (or an improvement in an existing one) you need to pass a test.

The current process (3rd option in tools menu) keeps on working as it was.

But we have drafted a new method because

– We need to be more explicit about the tests passed and the errors

– We need to improve the performance

So you can check the new method in the 4th option of the Tools menu

Besides this, the tests are very modular so if you are a python programmer you can use them in your own system because the code is being released or indeed you can write new tests that would be included in the official site. Make a PR on the data-models repo and we will add it eventually. Check this post.

New testing process in progress were you can contribute your code

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Current test process for new and extended data models

In order to approve a new data model a test needs to be passed. It cold be accessed in the 3rd option in the tools menu at the front page:

Pro: it is currently working

Con: It is mostly created in a single file for testing and error messages are not very explicit about the errors detected

The new process

1) Every test is an independent file:

2) To test the new data model it copies to local the files and then run the tests, which is quicker.

What can you do with basic knowledge of python (or with a good AI service)

Here you can see the current files available in the github repository data-models subdirectory test_data_model

Instructions

Updated all data models to the last version of json schema

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NOTE: We did yesterday 17-9 the changes. Unfortunately we made a mistake and now we have to revert all these changes, do it again properly and push. this Friday will be ready if not earlier.

NOTE2: It is already updated. Its Wednesday 15:30. Hopefully this time we made no errors.

The single-source-of-truth  of the data models is the json schema (file schema.json). This json schema has a tag ‘$schema’ indicating the meta schema the schema is compliant with.

Now all data models have been updated to the last one “https://json-schema.org/draft/2020-12/schema”

Therefore some errors provided by validators due to the obsolete previous value have been removed.

Thanks to the user Elliopardad in GitHub for its contribution and to the community of json schema for its support.

As we announce earlier we are one of the project listed in its global landscape of projects.

pydantic export now available

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The directory /code/ (see image with one example)  in every data model has now a new draft export the pydantic export.

Pydantic is a Python library that provides data validation and settings management using Python type annotations, allowing you to define data models that enforce type constraints and validate data automatically.

Now in most (if not all) data models you have such export to use it freely. Mind that is a first version and errors could happen (It is welcomed if you find any error or just make a suggestion)