Certainly! Let's create a similar integration using the Go programming language. Go (or Golang) is known for its performance and efficiency, making it a great choice for IoT applications.

Using Go With Coreflux

Coreflux's versatility extends to the Go programming language, allowing for efficient and performant integrations. In this section, we'll demonstrate how to integrate Coreflux with Go, using the Siemens PLC S7-1200 as our example.

Setting Up the Environment

First, ensure you have Go installed on your machine. If not, you can download and install it from the official Go website.

Next, we'll use the eclipse/paho.mqtt.golang library for MQTT communication in Go. Install it using:

go get github.com/eclipse/paho.mqtt.golang

Installing and Configuring a Flux Asset

Here's how you can install and configure the coreflux_S7mqtt asset using Go:

package main

import (
    "fmt"
    mqtt "github.com/eclipse/paho.mqtt.golang"
)

var server = "YOUR_COREFLUX_SERVER_IP"
var topicCommand = "$SYS/Coreflux/Command"

func main() {
    opts := mqtt.NewClientOptions().AddBroker(server).SetClientID("go-client")

    client := mqtt.NewClient(opts)
    if token := client.Connect(); token.Wait() && token.Error() != nil {
        panic(token.Error())
    }

    // Install the asset
    token := client.Publish(topicCommand, 0, false, "-I coreflux_S7mqtt")
    token.Wait()

    // ... Continue with other commands as needed

    client.Disconnect(250)
}

For retrieving the configuration example, publishing the modified JSON, and starting the asset, you can follow a similar pattern as shown above, adjusting the payload of the Publish method accordingly.

Integrating an ML Model for a Temperature Sensor

Assuming you have a Go-based ML model or a function that predicts anomalies based on temperature readings:

package main

import (
    "fmt"
    mqtt "github.com/eclipse/paho.mqtt.golang"
)

var server = "YOUR_COREFLUX_SERVER_IP"
var topicSensor = "path_to_your_S7_topic/MD3000"

func isAnomaly(temperature float64) bool {
    // Placeholder function. Replace with your ML model's prediction logic.
    return temperature > 30.0
}

func onMessage(client mqtt.Client, msg mqtt.Message) {
    temperature := string(msg.Payload()) // Convert payload to temperature value
    if isAnomaly(temperature) {
        fmt.Printf("Anomaly detected at temperature: %s\n", temperature)
    }
}

func main() {
    opts := mqtt.NewClientOptions().AddBroker(server).SetClientID("go-client")
    opts.OnConnect = func(c mqtt.Client) {
        if token := c.Subscribe(topicSensor, 0, onMessage); token.Wait() && token.Error() != nil {
            fmt.Println(token.Error())
            return
        }
    }

    client := mqtt.NewClient(opts)
    if token := client.Connect(); token.Wait() && token.Error() != nil {
        panic(token.Error())
    }

    select {} // Keep the program running
}

In this Go example, the program subscribes to the MQTT topic corresponding to the MD3000 memory location in the S7-1200 PLC. When a new temperature reading is received, it's passed to the isAnomaly function to check for anomalies.