print(f\"Robot {self.id}: Signal filtered to {filtered_signal}\")
return filtered_signal
# Amplify the signal if it's above a certain threshold
def amplify_signal(self, signal):
amplification_factor = np.random.uniform(1.5, 2.0) # Random amplification within range
amplified_signal = signal * amplification_factor
print(f\"Robot {self.id}: Amplified signal to {amplified_signal}\")
return amplified_signal
# check for cell damage and initiate repair if needed
def check_and_repair(self, signal):
if signal ＜ 0:
self.repair_mode = true
print(f\"Robot {self.id}: damaged tissue detected, initiating repair.\")
return self.perform_repair(signal)
return signal
# perform cell repair process
def perform_repair(self, signal):
print(f\"Robot {self.id}: Repairing damaged cells...\")
restored_signal = np.abs(signal) + np.random.uniform(10, 20) # Restore to positive signal range
print(f\"Robot {self.id}: Repair plete. Restored signal: {restored_signal}\")
self.repair_mode = False
return restored_signal
# Simulation of the nanorobot handling neural signals
def run_experiment:
signal_values = np.random.uniform(-5, 10, 5) # Generate random signals with possible damage indicators
robot = NanoRobot(id=204)
for signal in signal_values:
print(f\"Input signal: {signal}\")
filtered_signal = robot.capture_signal(signal)
final_signal = robot.check_and_repair(filtered_signal)
print(f\"Final processed signal: {final_signal}\\")
if __name__ == \"__main__\":
run_experiment 下面的代码注释写道：纳米机器人通过检测体内的神经信号，能够对异常或受损的信号进行捕捉和分析。首先，机器人会对神经信号进行过滤，去除噪音和干扰，从而得到更精准的神经反馈。当信号强度低于设定阈值时，机器人会自动跳过放
