Homematic Wetterstation mit NodeRed und MongoDB

In einem kleinen Bastelprojekt habe ich mich mit der Anbindung der Homematic Wetterstation an NodeRed beschäftigt um ein Dashboard zu bauen, das alle signifikanten Werte anzeigt. Am Ende habe ich noch eine Schippe draufgelegt und speichere alle Werte einmal pro Minute in einer MongoDB als json-File. Der Plan für das nächste Projekt steht schon. Die Visualisierung der Daten. Aber das kommt dann im nächsten Schritt. Hier könnt Ihr lesen was bisher geschah:

Die Hardware

Um es einfach zu halten war die Überlegung eine vorhandene Hardware zu nehmen. Die erste Überlegung war ein Raspberry Pi. Nachdem dann aber mit NodeRed und MongoDB schon zwei Anwendungen auf dem Plan standen und klar war, daß es wahrscheinlich mehr wird fiel meine Wahl auf die vorhandene QNAP mit der App ContainerStation. Die ist schnell installiert und stellt eine komplette Docker-Umgebung zur Verfügung und ist einfach zu verwalten. Außerdem ist der zusätzliche Stromverbrauch nicht relevant. So habe ich erstmal zwei Container erstellt:

Damit war der erste Schritt gemacht. In ein paar langen Abenden mit viel Testen, so manchen Fehlversuch entstand dann der NodeRed-Flow. An dieser Stelle erkläre ich den einen oder anderen Abschnitt. Wer mehr wissen will darf mich gerne kontaktieren. Den kompletten Quellcode findet Ihr am Ende des Artikels.

 

Zum grundsätzlichen Design

Anhand des oben gezeigten Ausschnitts eine kurze Erklärung was passiert. Der Injektor triggert einmal pro Minute das Auslesen der Daten von der Station. Ganz oben ist noch eine Anzeige von Datum und Zeit mit der Info, daß gerade eine Aktualisierung stattgefunden hat. Darunter seht Ihr ein paar Beispiele. Der Rest der Werte funktioniert sinngemäß genau so.

Temperatur Nordseite ist mein Carport (ein separater Sensor), Temperatur Wetterstation der Sensor in der Wetterstation und Luftfeuchtigkeit kommt ebenfalls aus der Wetterstation. Alle werden vom Injektor minütlich getriggert und geben ihre Werte direkt an das Dashboard aus.

Dazu werden diverse Werte in globalen Variablen gespeichert. Die werden dann später zusammen ausgelesen und an MongoDB gesandt. Die Erklärung wie das funktioniert findet Ihr dann unten.

Noch ein Hinweis: die Darstellung der Windrichtung habe ich hier gefunden.

Die Übergabe an MongoDB

Der Screenshot oben zeigt nun die Übergabe an MongoDB. Getriggert wird der Prozess vom gleichen Injektor, nur mit einer Sekunde Verzögerung weil ich festgestellt habe, daß die Laufzeit der einzelnen Teilprozesse nicht unendlich schnell ist, sondern eben eine Weile braucht. Mit der Sekunde Verzögerung läuft es stabil. Dieser Schritt liest nun alle vorher gespeicherten Werte aus und schiebt sie in den MongoDB-Connector. Den Inhalt von Read global all sieht so aus.

msg.payload= {
    "dataset":      "weather@home",
    "datetime":     new Date().toISOString().replace("Z", ""),
    "speed":        global.get("wind_speed"),
    "degrees":      global.get("wind_degrees"),
    "temperature":  global.get("temperature"),
    "humidity":     global.get("humidity"),
    "rain":         global.get("rain"),
    "sunshine":     global.get("sunshine")
}
return msg;

Zur Erklärung:

• dataset dient dazu den Datensatz-Typ zu indentifizieren, falls man mal auf die Idee kommt noch weitere Daten in die gleiche MongoDB-Datenbank zu kippen.
• datetime baut einfach einen lesbaren Timestamp um das aktuelle Datum und die Uhrzeit in den Datensatz zu schreiben. Erbsenzähler werden bemerken, daß es eine Sekunde zu spät ist.
• Die weiteren Elemente erklären sich selbst.

Die MongoDB-Daten

Schaut man mit MongoDB-Compass nun in die Daten findet man folgendes:

{"_id":{"$oid":"5f26f5c16b2b680018b5737f"},"dataset":"weather@home","datetime":"2020-08-02T17:20:01.981","speed":5.2,"degrees":115,"temperature":25.7,"humidity":53,"rain":21.8}

Die ID wird automatisch generiert. Der Rest erklärt sich wohl selbst. Am 2.8.2020 um 17:20 kam der Wind aus Südost (115°) mit 5,2 km/h. Die Temperatur betrug 25,7°C und die relative Luftfeuchtigkeit 53%. Das mit dem Regen ist so eine Sache. Die Wetterstation kumuliert nur, so daß man am Ende immer im Diagramm nachsehen muss was sich verändert hat. So sieht das am Ende dann aus. Bei Regen sieht man den leichten Knick, da hab ich noch keine Zeit gefunden es schöner darzustellen.

Der MongoDB-Connector

Weil ich eine Weile daran rumgefummelt habe, hier auch noch die Konfiguration des MongoDB-Connectors:

Der Quellcode

Ihr könnt den Quellcode einfach mit Copy-Paste in NodeRed übernehmen. Es wird erst einmal Fehlermeldungen hageln, weil Libraries fehlen. Ihr braucht folgende Nodes:

• node-red-contrib-ccu : ccu-get-value
• node-red-contrib-calc : calculator
• node-red-dashboard
• node-red-contrib-mongodb2 : mongodb2 in
• und was ich noch vergessen habe… 

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