Python語言作為一種高級編程語言，常常被用在各種科學計算和數據分析的領域中。其獨特的特性、易讀性、可擴展性以及開源性為Python在工業中的應用提供了極大的可能性。本篇文章將從Python語言在自動化控制、機械人、物聯網、圖像識別、大數據分析五個方面進行探討。
一、自動化控制
自動化控制作為一個龐大的業界，在各個行業中都扮演着重要的角色。Python語言自身的優秀特性和充足的庫支持為自動化控制提供了有力的技術支撐。Python語言擁有了自己的控制系統庫，比如：PyControl庫。下面我們用PyControl庫來完成一個簡單的例子，使用Python實現二級負反饋調節器：
from pycontrol import *
import numpy as np
def cascade_pid_demo():
#===================================
# Physical System Description
#===================================
Kq = 0.33
Ks = 19.8
Ke = 0.8
Jl = 0.0001
Bl = 0.0001
Jr = 0.0116
Br = 0.0120
Bm = 0.0127
Kb = 0.567
Kt = 0.042
Rm = 2.92
Lm = 0.00294
Tau_s = 0.001
Tau_m = 0.03
#===================================
# Controller Description
#===================================
Kp_c = 1
Ki_c = 1
Kd_c = 1
gamma_c = 1
Kp_sat = 1000
Ki_sat = 1000
Kd_sat = 1000
#===================================
# Synthetic System Description
#===================================
Ap = np.array([[0, 1], [-Ks/Jl, -Bl/Jl]])
Cp = np.array([[0, 1]])
Bp = np.array([[0], [1/Jl]])
Dp = np.array([[0]])
sys_P = StateSpace(Ap, Bp, Cp, Dp)
Am = np.array([[0, 1], [-Kt/(Jr*Tau_m), -Br/Jr]])
Cm = np.array([[0, 1]])
Bm = np.array([[-Kb/(Jr*Tau_m)], [0]])
Dm = np.array([[0]])
sys_M = StateSpace(Am, Bm, Cm, Dm)
Aq = np.array([[0, 1],[-Ke*Kq/(Jl), -(Bl + Ke*Ke*Kq)/(Jl)]])
Cq = np.array([[0, 1]])
Bq = np.array([[0], [Kq/(Jl)]])
Dq = np.array([[0]])
sys_Q = StateSpace(Aq, Bq, Cq, Dq)
tau = Tau_s
sys1 = Cascade(sys_P, sys_M, sys_Q, tau)
#===================================
# Control System Description
#===================================
Kp = Kp_c
Ki = Ki_c
Kd = Kd_c
gamma = gamma_c
Kp_s = Kp_sat
Ki_s = Ki_sat
Kd_s = Kd_sat
sys_CL = ClosingLoop(sys1, Kp, Ki, Kd, Kp_s, Ki_s, Kd_s, gamma)
sys_CL_name = "Cascade PID Controlled System"
# Plot the step response of the control system
sys_CL.PlotStepResponse(0.0,20.0,stepSize=0.01,Name=sys_CL_name)
if __name__ == '__main__':
cascade_pid_demo()
二、機械人
Python不僅可以實現智能化控制，還可以實現科學計算和數據分析方面的機械人項目。Python語言的科學計算的數值模塊，如NumPy和matplotlib等，使計算和繪圖變得輕鬆而快捷。下面我們使用Python實現一個人工神經網絡的機械人項目。
import numpy as np
import matplotlib.pyplot as plt
import random
class NeuralNetwork():
def __init__(self):
random.seed(1)
self.synaptic_weights = 2 * random.random((3, 1)) - 1
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(self, x):
return x * (1 - x)
def train(self, training_inputs, training_outputs, training_iterations):
for iteration in range(training_iterations):
output = self.think(training_inputs)
error = training_outputs - output
adjustment = np.dot(training_inputs.T, error * self.sigmoid_derivative(output))
self.synaptic_weights += adjustment
def think(self, inputs):
return self.sigmoid(np.dot(inputs, self.synaptic_weights))
if __name__ == "__main__":
neural_network = NeuralNetwork()
print("Random starting synaptic weights: ")
print(neural_network.synaptic_weights)
training_inputs = np.array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_outputs = np.array([[0, 1, 1, 0]]).T
neural_network.train(training_inputs, training_outputs, 15000)
print("Synaptic weights after training: ")
print(neural_network.synaptic_weights)
new_inputs = np.array([1, 0, 0])
output = neural_network.think(new_inputs)
print("Output of robotic brain:---> ")
print(output)
三、物聯網
Python語言在物聯網行業中常用於物聯網平台的構建、數據的處理。作為一種簡單易學、語法輕鬆、代碼可讀性好的編程語言，Python語言在物聯網領域中得到了越來越多的應用。下面我們以火災預警識別系統為例，展示Python語言在物聯網行業中的應用：
import time
import threading
class Smoke_Sensor(threading.Thread):
def __init__(self, thread_name, sensor_delay, concentrations):
threading.Thread.__init__(self)
self.thread_name = thread_name
self.sensor_delay = sensor_delay
self.concentrations = concentrations
self.smoke_threshold = 100
print("Smoke_Sensor Starting:" + self.thread_name)
def run(self):
print("Starting " + self.thread_name + " thread....")
while True:
for index in range(0, len(self.concentrations)):
if self.concentrations[index] > self.smoke_threshold:
print("\nSmoke_Alert! The concentration of toxic gases -", index + 1, " exceeds the threshold limit!!!\n\n\n")
time.sleep(self.sensor_delay)
class Temperature_Sensor(threading.Thread):
def __init__(self, thread_name, sensor_delay, temperature_values):
threading.Thread.__init__(self)
self.thread_name = thread_name
self.sensor_delay = sensor_delay
self.temperature_values = temperature_values
self.temperature_threshold = 70
print("Temperature_Sensor Starting:" + self.thread_name)
def run(self):
print("Starting " + self.thread_name + " thread....")
while True:
for index in range(0, len(self.temperature_values)):
if self.temperature_values[index] > self.temperature_threshold:
print("\nTemperature_Alert! The temperature -", index + 1, " exceeds the threshold limit!!!\n\n\n")
time.sleep(self.sensor_delay)
if __name__ == '__main__':
# The predefined simulated data feed for sensors
smoke_concentrations = [10, 12, 22, 65]
temperature_values = [65, 72, 77, 85]
# Creating Smoke Sensor Object
smoke_sensor = Smoke_Sensor("Smoke_Sensor_Thread", 30, smoke_concentrations)
smoke_sensor.start()
# Creating Temperature Sensor Object
temperature_sensor = Temperature_Sensor("Temperature_Sensor_Thread", 60, temperature_values)
temperature_sensor.start()
四、圖像識別
圖像識別，作為一種先進的技術，為各個領域提供了很多優秀的應用。Python語言最重要的庫之一就是OpenCV，它是具有超高性能的圖像識別和處理庫。利用Python語言和OpenCV庫的強大功能，可以輕鬆實現各種圖像識別項目。下面我們用Python和OpenCV庫來實現基礎的圖像識別程序：
import cv2
def show_webcam(mirror=False):
cam = cv2.VideoCapture(0)
while True:
ret_val, img = cam.read()
if mirror:
img = cv2.flip(img, 1)
cv2.imshow('my webcam', img)
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()
def main():
show_webcam(mirror=True)
if __name__ == '__main__':
main()
五、大數據分析
Python語言作為一種數據分析的語言，其常用的數據分析庫NumPy、Matplotlib等在大數據領域中都表現出了卓越的能力。應用Python進行數據分析，特別是對海量數據進行分析和預測，具有簡單、快速、高效的特點。下面我們用Python語言中的pandas庫實現數據分析：
import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.randn(1000, 4), columns=['A', 'B', 'C', 'D'])
print(data.tail())
df = pd.DataFrame(np.random.randn(1000, 4), columns=['A', 'B', 'C', 'D'])
print(df.head(3))
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
print(df.sort_index(axis=1, ascending=False))
原創文章，作者：JBOKW，如若轉載，請註明出處：https://www.506064.com/zh-hk/n/375306.html
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