import torch
import torch.nn as nn
import random

class FirefightingDrone:
def __init__(self, location=[0, 0], battery=100, water_tank=100, chemo_sensor=False):
self.location, self.battery, self.water_tank, self.chemo_sensor = location, battery, water_tank, chemo_sensor

def move_to(self, target_location):
self.location, self.battery = target_location, self.battery - 1

def can_return_home(self):
return self.battery >= 20

def extinguish_fire(self, fire_location):
water_needed = fire_location.intensity
self.water_tank -= water_needed
fire_location.extinguish(water_needed)

def detect_fire(self, fire_location):
return self.chemo_sensor and fire_location.chemicals

class Fire:
def __init__(self, location, intensity, chemicals=[]):
self.location, self.intensity, self.chemicals = location, intensity, chemicals

def extinguish(self, water_needed):
self.intensity -= water_needed

class FireModel(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(nn.Linear(10, 10), nn.ReLU(), nn.Linear(10, 10), nn.ReLU(), nn.Linear(10, 10), nn.Softmax(dim=-1))

def forward(self, x):
return self.layers(x)

class FireSpreadPredictor(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(nn.Linear(10, 20), nn.ReLU(), nn.Linear(20, 10), nn.ReLU(), nn.Linear(10, 1))

def forward(self, x):
return self.layers(x)

class FireAlgorithm:
def __init__(self, num_drones, model):
self.num_drones, self.model = num_drones, model
self.drones = [FirefightingDrone() for _ in range(num_drones)]
self.fires = [Fire(location=[random.randint(0, 100), random.randint(0, 100)], intensity=random.randint(1, 10)) for _ in range(num_drones)]

def deploy(self):
for drone in self.drones:
input_tensor = torch.tensor(drone.location + [drone.water_tank]).float()
chosen_fire = self.fires[torch.argmax(self.model(input_tensor)).item()]
drone.move_to(chosen_fire.location)
drone.extinguish_fire(chosen_fire)

class GameTheoryOptimizer:
def __init__(self, model, fire_predictor):
self.model, self.fire_predictor = model, fire_predictor

def optimize_strategy(self, drones, fires):
strategies = []
for drone in drones:
current_state = self.get_state(drone, fires)
predicted_fire_spread = self.fire_predictor(current_state)
strategy = self.calculate_best_response(drone, predicted_fire_spread)
strategies.append(strategy)
return strategies

def get_state(self, drone, fires):
# Compile information about the drone, fires, and environment
return torch.tensor([...])

def calculate_best_response(self, drone, predicted_fire_spread):
# Use game theory to calculate the best response to the predicted fire spread
return best_strategy

# Example usage
fire_model = FireModel()
fire_predictor = FireSpreadPredictor()
fire_algorithm = FireAlgorithm(num_drones=10, model=fire_model)
game_theory_optimizer = GameTheoryOptimizer(model=fire_model, fire_predictor=fire_predictor)

# Deploy the drones to extinguish fires
fire_algorithm.deploy()

# Optimize strategy using game theory
strategies = game_theory_optimizer.optimize_strategy(fire_algorithm.drones, fire_algorithm.fires)


HydroScarab Firefighting Software
Overview:
HydroScarab is a comprehensive software designed to simulate and control firefighting drones. Leveraging PyTorch for deep learning, the software includes chemical detection, fire spread prediction, and game theory-based optimization to ensure efficient fire extinguishing.

Classes:
FirefightingDrone: Represents a drone equipped with a water tank, battery, and optional chemo sensor to detect chemicals produced by fires.

move_to(target_location): Move the drone to the target location.
can_return_home(): Check if the drone can return home.
extinguish_fire(fire_location): Extinguish fire at the given location.
detect_fire(fire_location): Detect fire based on chemical signature.
Fire: Represents a fire with location, intensity, and chemicals produced.

extinguish(water_needed): Reduce fire intensity by water needed.
FireModel: Neural network model to help drones decide where to extinguish fires.

FireSpreadPredictor: Neural network model to predict the spread of fire.

FireAlgorithm: Implements the deployment of drones to extinguish fires.

deploy(): Deploy drones to extinguish fires.
GameTheoryOptimizer: Uses game theory to calculate the best response strategy for drones based on predicted fire spread.

optimize_strategy(drones, fires): Optimize drone strategies using game theory.
Example Usage:
python
Copy code
fire_model = FireModel()
fire_predictor = FireSpreadPredictor()
fire_algorithm = FireAlgorithm(num_drones=10, model=fire_model)
game_theory_optimizer = GameTheoryOptimizer(model=fire_model, fire_predictor=fire_predictor)

# Deploy the drones to extinguish fires
fire_algorithm.deploy()

# Optimize strategy using game theory
strategies = game_theory_optimizer.optimize_strategy(fire_algorithm.drones, fire_algorithm.fires)
Dependencies:
PyTorch
The HydroScarab software embodies fast, adaptive, and modular design principles, inspired by the pyrophyllus beetle. It is primed for simulation and real-world applications in firefighting drone control.