pytorch_environmental_monitoring.py

# Python script for implementing environmental monitoring using the MAGE framework with PyTorch,

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import random

# Define the EnvironmentalRobot class to represent individual environmental monitoring robots
class EnvironmentalRobot:
    def __init__(self, robot_id, sensors):
        self.robot_id = robot_id
        self.sensors = sensors
        self.location = None
        self.observations = []
        self.log = []

    def move(self, new_location):
        # Move the robot to a new location
        self.location = new_location
        self.log.append(f"Robot {self.robot_id} moved to {new_location}")

    def gather_data(self, data):
        # Gather environmental data using the robot's sensors
        self.observations.append(data)
        self.log.append(f"Robot {self.robot_id} gathered data: {data}")

    def track_wildlife(self, wildlife_location):
        # Track wildlife movement based on the detected location (simulated)
        print(f"Robot {self.robot_id} is tracking wildlife at location: {wildlife_location}")
        self.log.append(f"Robot {self.robot_id} tracked wildlife at location {wildlife_location}")

    def detect_threat(self, observation):
        # Detect potential environmental threats based on the observation (simulated)
        if np.random.random() < 0.1:
            print(f"Robot {self.robot_id} detected a potential environmental threat!")
            self.log.append(f"Robot {self.robot_id} detected a potential environmental threat!")

# Define the Forest class to represent the forest environment
class Forest:
    def __init__(self, num_robots, grid_size, wildlife_locations):
        self.num_robots = num_robots
        self.grid_size = grid_size
        self.wildlife_locations = wildlife_locations
        self.robots = self.deploy_robots()
        self.steps_log = []

    def deploy_robots(self):
        # Deploy environmental monitoring robots with random sensors
        robots = []
        for i in range(self.num_robots):
            sensors = np.random.choice(['camera', 'temperature', 'humidity', 'air_quality'], size=2, replace=False)
            robot = EnvironmentalRobot(i, sensors)
            robots.append(robot)
        return robots

    def simulate_monitoring(self, num_steps):
        # Simulate the environmental monitoring process
        for step in range(num_steps):
            step_log = f"Step {step + 1}:\n"
            for robot in self.robots:
                # Move the robot to a random location in the forest
                x = np.random.randint(0, self.grid_size[0])
                y = np.random.randint(0, self.grid_size[1])
                robot.move((x, y))

                # Gather environmental data at the current location
                data = self.get_environmental_data(robot.location)
                robot.gather_data(data)

                # Track wildlife if detected
                if robot.location in self.wildlife_locations:
                    robot.track_wildlife(robot.location)

                # Detect potential environmental threats
                robot.detect_threat(data)

            # Share data and insights among robots (simulated)
            self.share_data_and_insights()
            self.steps_log.append(step_log)

    def get_environmental_data(self, location):
        # Generate simulated environmental data at the given location
        temperature = np.random.randint(20, 31)
        humidity = np.random.randint(40, 81)
        air_quality = np.random.randint(0, 101)
        data = {'temperature': temperature, 'humidity': humidity, 'air_quality': air_quality}
        return data

    def share_data_and_insights(self):
        # Simulate sharing of data and insights among robots
        print("Robots are sharing environmental data and insights...")

    def detailed_logs(self):
        # Print detailed logs for each robot
        for robot in self.robots:
            for log_entry in robot.log:
                print(log_entry)

# Define a PyTorch Dataset for training a model (if needed)
class EnvironmentalDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        sample = self.data[idx]
        label = self.labels[idx]
        return sample, label

# Define a neural network model for data analysis
class EnvironmentalModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(EnvironmentalModel, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.sigmoid(self.fc2(x))
        return x

# Train the model (dummy training loop)
def train_model(model, dataloader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for data, labels in dataloader:
            inputs = data
            targets = labels
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
        print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {loss.item()}")

# Define additional helper functions for environmental monitoring
def analyze_data_distribution(data):
    data_stats = {}
    for key in data[0].keys():
        values = [entry[key] for entry in data]
        data_stats[key] = (np.mean(values), np.std(values))
    return data_stats

def visualize_data_distribution(data_stats):
    print("Data Distribution:")
    for key, stats in data_stats.items():
        mean, std = stats
        print(f"{key}: Mean = {mean}, Std Dev = {std}")

def save_model(model, filepath):
    torch.save(model.state_dict(), filepath)
    print(f"Model saved to {filepath}")

def load_model(model, filepath):
    model.load_state_dict(torch.load(filepath))
    model.eval()
    print(f"Model loaded from {filepath}")

# Main script execution
if __name__ == "__main__":
    # Set the random seed for reproducibility
    torch.manual_seed(42)
    np.random.seed(42)

    # Define the simulation parameters
    num_robots = 5
    grid_size = (10, 10)
    wildlife_locations = [(2, 3), (5, 7), (8, 1)]
    num_steps = 20

    # Create an instance of the Forest environment
    forest = Forest(num_robots, grid_size, wildlife_locations)

    # Simulate the environmental monitoring process
    forest.simulate_monitoring(num_steps)

    # Print the log of actions taken during the simulation
    for log in forest.steps_log:
        print(log)

    # Dummy data for the dataset
    data = np.random.rand(100, 10)
    labels = np.random.randint(0, 2, size=(100, 1))
    dataset = EnvironmentalDataset(data, labels)
    dataloader = DataLoader(dataset, batch_size=10, shuffle=True)

    # Initialize the model, criterion, and optimizer
    model = EnvironmentalModel(input_size=10, hidden_size=5, output_size=1)
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Train the model
    train_model(model, dataloader, criterion, optimizer, num_epochs=10)

    # Saving and loading the model
    model_path = "environmental_model.pth"
    save_model(model, model_path)
    load_model(model, model_path)

    # More detailed logging
    print("Detailed Logs:")
    forest.detailed_logs()

    # Summary of data collection by each robot
    print("Summary of Environmental Monitoring Operations:")
    for robot in forest.robots:
        print(f"Robot {robot.robot_id} collected the following observations:")
        for observation in robot.observations:
            print(f" - {observation}")

    # Additional functionality for monitoring and analytics
    collected_data = [obs for robot in forest.robots for obs in robot.observations]
    data_stats = analyze_data_distribution(collected_data)
    visualize_data_distribution(data_stats)

# Possible Errors and Solutions:
# 1. Error: No module named 'torch'
#    Solution: Make sure you have PyTorch installed. You can install it using pip: `pip install torch`.
#
# 2. Error: IndexError: index out of bounds
#    Solution: This error can occur if the robot's location is outside the grid boundaries.
#              Ensure that the robot's movement is restricted within the grid size.
#              Modify the `move` method in the `EnvironmentalRobot` class to handle boundary conditions.
#
# 3. Error: KeyError: 'temperature'
#    Solution: This error can happen if the environmental data dictionary is missing the 'temperature' key.
#              Make sure that the `get_environmental_data` method returns a dictionary with the expected keys.
#              Double-check the data generation code and ensure it matches the expected format.
#
# 4. Error: Inefficient wildlife tracking
#    Solution: If the wildlife tracking is not efficient or accurate, consider implementing more advanced algorithms.
#              This could involve using computer vision techniques, machine learning models, or specialized sensors.
#              Integrate real-time data processing and analysis to improve wildlife tracking capabilities.

# Note: The code provided here is a simplified simulation and may not cover all the complexities of real-world environmental
#       monitoring. In practice, you would need to integrate with the actual robot control systems, sensor data, and
#       environmental models to deploy the multi-agent system effectively. The simulation can be extended to incorporate
#       more realistic environmental conditions, wildlife behavior, and data analysis techniques.