fml40 python reference implementation
Reference implementation for building a Digital Twin based on the Forest Modeling Language 4.0 (fml40), as specified in this white paper (in German).
Installation
To use the fml40 reference implementation package in your own project, install it from Pypi
python -m pip install fml40-reference-implementationor from our self hosted wheel
pip install https://git.rwth-aachen.de/kwh40/fml40-reference-implementation/-/jobs/artifacts/master/raw/public/fml40_reference_implementation-0.2.4.4-py3-none-any.whl?job=wheelDocumentation
We provide a documentation to get start with the fml40 python reference implementation package. It can be found here.
Features
This package provides two different modes to launch your Digital Twins:
- Launching Digital Twin quickly in persistent mode via
fml40s.py. You need to register a thing identity for your Digital Twin via S³I Config REST API and prepare a respective config file that describes a fml40-compliant DT in JSON. For more details, refer to the exampleMyDigitalTwin.jsonin the sectionConfig files. - Extending the capabilities with user defined Digital Twins, which means you can additionally define and insert a function according to ForestML 4.0 into the Digital Twin. We provide an exemplary Digital Twin built in Jupyter Notebook, refer to notebooks
08a,08b,08dand08e.
Usage
Using fml40s.py
A Digital Twin can be launched in persistent mode by executing
python fml40s.py launch <CONFIG_FILE>Config files for Digital Twin must be located in the folder configs.
For more options call just run
python fml40s.py -hDevelop your Digital Twin using this package
Here, we introduce briefly how to develop a user-specified Digital Twin. For more details, refer to the cloud/edge Digital Twin built in Jupyter Notebook.
The following steps can only be carried out after you have created the thing identity of your Digital Twin via S³I Config REST API and made a respective config file. Here, we use the config file MyDigitalTwin.json from section Config files. Now we continue the following steps:
- Optionally, set a logger using the function
setup_logger. The generated log file will then be located in the folderlogs.
ml.setup_logger("MyDigitalTwin")- Set the path of the config file and import it in JSON format.
config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "configs"))
dt_model = ml.load_config(config_filepath=os.path.join(config_path, "MyDigitalTwin.json"))
- Create the thing instance.
client_secret = "" #To be filled
thing = ml.create_thing(
model=dt_model, grant_type="client_credentials", secret=client_secret,
is_broker_rest=False, is_broker=True, is_repo=True
)- As specified in the config file, the Digital Twin has a feature named fml40::ProvidesProductionData. In the next step, we extend/implement this class with our own logic.
class ProvidesProductionDataImpl(ProvidesProductionData):
def __init__(self, name="", identifier=""):
super(ProvidesProductionData, self).__init__(
name=name,
identifier=identifier)
self.production_data_list = thing.features["ml40::ProductionData"].subFeatures["ml40::Composite"].targets
def getProductionData(self, name):
for key in self.production_data_list.keys():
if key == name:
return self.production_data_list[key].to_subthing_json()
return {"error": "NOT FOUND"}
- Insert the class into the respective feature of your Digital Twin
ml.add_function_impl_obj(thing, ProvidesProductionDataImpl, "fml40::ProvidesProductionData")- Launch your Digital Twin
thing.run_forever()So far, your Digital Twin has been created. In the meantime, you can establish a connection with it using S³I-B messages.
Config files
A valid configuration consists of exactly one JSON object. Mandatory fields of the JSON object are
- thingId
- policyId and
- attributes
The configuration file must be created in accordance with ForestML 4.0, refer to Cap. 7.1 of the KWH4.0 ForestML 4.0 white paper. Additionally, we provide an exemplary JSON config file, see below.
MyDigitalTwin.json:
{
"thingId": "s3i:id",
"policyId": "s3i:id",
"attributes": {
"class": "ml40::Thing",
"name": "my digital twin",
"roles": [
{
"class": "fml40::Harvester"
}
],
"features": [
{
"class": "fml40::ProvidesProductionData"
},
{
"class": "ml40::ProductionData",
"subFeatures": [
{
"class": "ml40::Composite",
"targets": [
{
"class": "ml40::Thing",
"name": "Stammsegment 4711",
"roles": [
{
"class": "fml40::StemSegment",
}
],
"features": [
{
"class": "ml40::Location",
"latitude": 50.1231,
"longitude": 6.1231
},
{
"class": "fml40::StemSegmentProperties",
"diameter": 0.4,
"length": 2,
"woodType": "Spruce"
}
]
},
{
"class": "ml40::Thing",
"name": "Stammsegment 4712",
"roles": [
{
"class": "fml40::StemSegment",
}
],
"features": [
{
"class": "ml40::Location",
"latitude": 50.1231,
"longitude": 6.1231
},
{
"class": "fml40::StemSegmentProperties",
"diameter": 0.6,
"length": 3,
"woodType": "Spruce"
}
]
}
]
}
]
},
{
"class": "ml40::Location",
"longitude": 5.03424,
"latitude": 52.52345
}
]
}
}Structure
Configs
This folder contains configuration files in JSON format for Digital Twins.
ml
This directory includes the implementations of the fml40 python reference implementation as described in the KWH ForestML 4.0 white paper.
demo
Example for creating and launching an HMI, which is used to communicate with the permanent Digital Twin using S³I-B protocol.
logs
This folder contains the log files of the created and launched Digital Twins.
tests
This folder contains all test scripts (currently in development).