Build your smart terminal with Raspberry Pi

Author: Zen and the Art of Computer Programming

1 Introduction

Smart terminals are new computing devices that have become popular in recent years. They have the characteristics of high computing performance, long-term running ability, data processing capabilities, etc., and are wearable, diversified configurations, easy to deploy and update. Compared with ordinary traditional terminal products, the biggest advantage of smart terminals is their high degree of integration and autonomy. Users can customize personalized applications at any time according to their own preferences to realize functions such as interactive interaction, automated control, information collection and analysis. Through the advancement of technologies such as the Internet of Things, cloud computing, and artificial intelligence, smart terminals are becoming a new and emerging product in the field of Internet of Things.
In this article, we will introduce how to use Raspberry Pi as a development platform for smart terminals to implement a simple personal assistant robot. Raspberry Pi is an open source low-cost, low-priced single-board computer developed by British Dom Lincoln. It is based on ARM architecture and has more than ten interfaces for connecting peripherals. It is also widely used in various DIY projects, education fields, Internet of Things fields, media players and other scenarios.
This article assumes that readers have some relevant knowledge base, including programming languages, computer basics, Linux systems and their command operations. In addition, this article will not introduce too much about the Raspberry Pi operating system. If you want to know more, it is recommended to read the relevant information.

2. Explanation of basic concepts and terms

Linux system
Linux system is currently the most popular server operating system. It has become the operating system for various network devices such as personal computers, routers, and servers. Due to its openness and free nature, many companies have chosen Linux-based server platforms.
Python
Python is a popular high-level programming language whose simple syntax and strong library support make it the language of choice for many startups.
Raspberry Pi
Raspberry Pi (RPi for short) is an open source single-board computer for beginners, designed and developed by British Dom Lincoln. It was originally created by the British Raspberry Pi Foundation (RPIF) and was later transferred to and funded by the British open source community Sponsorware. Raspberry Pi 3 was released in July 2014. It has a history of fifteen years and has experienced rapid development in various application scenarios from creative gadgets to smart homes, military drones, laptops, etc.
GPIO
General Purpose Input/Output (General Purpose Input/Output) is a type of pin provided by Raspberry Pi and is used to drive external sensors, LEDs, buttons, buzzers, speakers, etc. GPIO can realize various functions through programming, such as controlling motors, spraying water, adjusting temperature, opening and closing doors and windows, etc.
ADC (Analog to Digital Converter)
ADC stands for Analog to Digital Converter, which converts analog signals into digital signals. The Raspberry Pi's ADC module provides a 10-bit precision ADC output, suitable for measuring various voltage changes.
PWM (Pulse Width Modulation)
PWM stands for Pulse Width Modulation, which is pulse width modulation. By modulating the time of pulse emission, it can be used to drive motors, control volume, change light brightness, etc.
UART
UART stands for Universal Asynchronous Receiver/Transmitter, which is a universal asynchronous receiver and transmitter. UART can achieve two-way communication and is suitable for transmitting data of multiple protocols.
TCP/IP protocol
TCP/IP protocol refers to the Transmission Control Protocol/Internet Protocol, which defines the rules for communication between computers.
MQTT (Message Queuing Telemetry Transport)
MQTT is a lightweight publish/subscribe message transmission protocol in the field of Internet of Things.
Docker Containers
Docker containers are lightweight virtualization technology that can run applications in an isolated environment.
TensorFlow
TensorFlow is an open source machine learning framework that helps developers train neural network models.
API (Application Programming Interface)
The full name of API is Application Programming Interface, which is application programming interface. It is a method of communication between different software components and a bridge between intelligent terminals and services.

3. Explanation of core algorithm principles, specific operating steps and mathematical formulas

Understanding the Raspberry Pi system architecture
First, we need to understand the Raspberry Pi system architecture. The Raspberry Pi is based on the Linux operating system, so overall it is an "operating system + software" whole. Raspberry Pi is usually divided into four boards, namely processor board, memory board, hard disk board and display board. The processor board is responsible for the work of the CPU, RAM, and GPU, the memory board is responsible for storing SD cards and flash memory, the hard disk board is responsible for connecting external USB hard disks or SSD solid state drives, and the display board is responsible for displaying the screen. In addition to these sectors, there are also a number of supporting peripherals, such as GPIO, ADC, PWM, UART, I2C, SPI, Ethernet, Bluetooth, etc.
Installing the Raspberry Pi operating system
Of course, we also need to install the Raspberry Pi operating system. Under normal circumstances, the Raspberry Pi will be pre-installed with a pre-compiled system image file, but if the system fails or crashes, we can solve the problem by burning a new image file. Normally, we only need to insert the USB flash drive and let the Raspberry Pi recognize it, and then the system image file can be written to the SD card. General system image files have their own corresponding boot disk burning tools, such as Win32DiskImager or dd command. In addition, we can also download the system image file through the Internet and then manually flash it.
Configure SSH remote login
SSH (Secure Shell) is a secure remote login method that allows us to access the Raspberry Pi through the network. By default, the Raspberry Pi system does not allow SSH remote login, so we need to enable the SSH service in the Raspberry Pi system. For security reasons, we can enable password verification instead of public key verification. After setting up, we can use the SSH client to connect to the Raspberry Pi through the command line.
Prepare the Python environment
In the Raspberry Pi system, we need to prepare the Python environment, because the Raspberry Pi system is pre-installed with Python 3.x version, and we are more accustomed to Python 2.x version. Therefore, we need to manually install the Python 2.x version. Under normal circumstances, we can download the source code package from the official website, then unzip it, and then configure the environment variables.
Using GPIO
GPIO (General Purpose Input/Output) on the Raspberry Pi system, that is, general purpose input/output, is a Raspberry DAP pin used to drive external sensors, LEDs, buttons, buzzers, speakers, etc. GPIO can realize various functions through programming, such as controlling motors, spraying water, adjusting temperature, opening and closing doors and windows, etc. GPIO is usually divided into two groups, one for controlling output signals and the other for reading input signals. GPIO programming is completed in two ways: one is to directly operate the GPIO pins, and the other is to complete by calling system library functions. For application scenarios that require frequent signal switching, the second method is more convenient and faster.
Read ADC data
ADC (analog-to-digital converter) is called Analog to Digital Converter, which converts analog signals into digital signals. The Raspberry Pi's ADC module provides a 10-bit precision ADC output, suitable for measuring various voltage changes. ADC programming is completed in two ways: one is to directly operate the ADC pins, and the other is to complete by calling system library functions. For application scenarios that require frequent reading of ADC data, the second method is more convenient and faster.
Set the PWM signal
PWM (pulse width modulation) is called Pulse Width Modulation, which is pulse width modulation. By modulating the time of pulse emission, it can be used to drive motors, control volume, change light brightness, etc. The setting of the PWM signal is completed in two ways: one is to directly operate the PWM pin, and the other is to complete by calling the system library function. For application scenarios that require frequent power control, the second method is more convenient and faster.
The full name of serial communication
UART is Universal Asynchronous Receiver/Transmitter, which is a universal asynchronous receiver and transmitter. UART can achieve two-way communication and is suitable for transmitting data of multiple protocols. UART communication protocols include RS232, RS485, TTL, CAN, FlexRay, etc. In the Raspberry Pi system, UART is completed by calling system library functions.
Create MQTT client
MQTT (Message Queuing Telemetry Transport) is a lightweight publish/subscribe message transmission protocol in the field of Internet of Things. We need to create an MQTT client to connect to the MQTT server. In the Raspberry Pi system, we can use the paho-mqtt library to create an MQTT client.
Running Docker Containers
Docker containers are lightweight virtualization technology that can run applications in an isolated environment. In a Raspberry Pi system we can run the official BusyBox or Alpine Linux containers, which are very small and easy to install. The Docker client that comes with the Raspberry Pi system can also help us manage Docker containers.
Training neural network models
TensorFlow is an open source machine learning framework that can help developers train neural network models. The Raspberry Pi system comes with TensorFlow support, and we can train neural network models to implement intelligent functions.
Create an API interface
Finally, we need to create an API interface that can communicate with the smart assistant application. The API interface can adopt RESTful style and can convert the received request into JSON format data. The communication protocol of the API interface can use HTTP, HTTPS, Websocket, etc. In the Raspberry Pi system, we can use Flask or Django to quickly build API interfaces.

4. Specific code examples and explanations

Identify the Raspberry Pi system architecture

import RPi.GPIO as GPIO
if __name__ == '__main__':
print('树莓派系统架构')
for line in open('/proc/cpuinfo').readlines():
if 'Revision' in line:
print(line)
print('
处理器类型:')
with open('/sys/firmware/devicetree/base/model', 'r') as f:
print(f.read())

Configure SSH remote login

sudo systemctl start sshd

Install Python

wget https://www.python.org/ftp/python/2.7.13/Python-2.7.13.tgz
tar -xzvf Python-2.7.13.tgz
cd Python-2.7.13
./configure --prefix=/usr/local
make && sudo make altinstall

Install pip

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py

Using GPIO

#!/usr/bin/env python
import time
import RPi.GPIO as GPIO
LED_PIN = 11  # LED 灯的引脚号
BUZZER_PIN = 13  # 蜂鸣器的引脚号
def setup():
"""设置 GPIO 模式"""
GPIO.setmode(GPIO.BOARD)    # 使用 BOARD 编号排列的引脚编号
GPIO.setup(LED_PIN, GPIO.OUT)   # 设置 LED 为输出模式
GPIO.output(LED_PIN, False)     # 将 LED 灯熄灭
GPIO.setup(BUZZER_PIN, GPIO.OUT) # 设置蜂鸣器为输出模式
GPIO.output(BUZZER_PIN, True)    # 打开蜂鸣器
def blink():
while True:
"""点亮 LED 灯"""
GPIO.output(LED_PIN, True)      # 点亮 LED 灯
time.sleep(1)                     # 等待 1 秒
GPIO.output(LED_PIN, False)     # 熄灭 LED 灯
time.sleep(1)                     # 等待 1 秒
def beep():
"""播放蜂鸣声"""
period = 0.5 / 1000          # 周期为 0.5ms
duty_cycle = 50             # 占空比 50%
on_time = 0                 # 开灯时间
while True:
GPIO.output(BUZZER_PIN, True)       # 打开蜂鸣器
time.sleep(on_time / 1000)            # 等待开灯时间
GPIO.output(BUZZER_PIN, False)      # 关闭蜂鸣器
t1 = time.time()                    # 获取当前时间戳
on_time += int((period * 1000) + (t1 % period))    # 更新开灯时间
if __name__ == '__main__':
try:
setup()                  # 设置 GPIO 模式
blink()                  # 点亮 LED 灯
beep()                   # 播放蜂鸣声
except KeyboardInterrupt:
pass                        # 中断程序执行
finally:
GPIO.cleanup()               # 清除 GPIO 状态

Read ADC data

#!/usr/bin/env python
import time
import os
import RPi.GPIO as GPIO
adc_channel = 0           # ADC 通道
sleep_time = 1000 / 1000   # 每秒读取一次 ADC 数据
def read_adc():
"""读取 ADC 数据"""
os.system("echo "+str(int(adc_channel))+"> /sys/bus/iio/devices/iio:device0/in_voltage"+str(int(adc_channel))+"_raw")
with open("/sys/bus/iio/devices/iio:device0/in_voltage"+str(int(adc_channel))+"_raw", "rb") as file:
data = file.read().strip()
return int(data[::-1].hex(), 16)/10**6*3.3
if __name__ == '__main__':
try:
while True:
voltage = read_adc()        # 读取 ADC 数据
print("ADC 通道 {0} 的值：{1:.2f}".format(adc_channel, voltage))
time.sleep(sleep_time)      # 睡眠指定时间
except KeyboardInterrupt:
pass                         # 中断程序执行

Set PWM signal

#!/usr/bin/env python
import time
import RPi.GPIO as GPIO
pwm_pin = 32              # PWM 引脚号
frequency = 50            # 频率为 50 Hz
dc_value = 75             # 占空比为 75%
def set_pwm():
"""设置 PWM 信号"""
GPIO.setwarnings(False)                          # 关闭警告提示
GPIO.setmode(GPIO.BOARD)                         # 使用 BOARD 编号排列的引脚编号
GPIO.setup(pwm_pin, GPIO.OUT, initial=GPIO.LOW)  # 设置 PWM 引脚为输出模式，初始值为 0%
p = GPIO.PWM(pwm_pin, frequency)                  # 设置 PWM 频率和占空比
p.start(dc_value)                                # 启动 PWM 信号
def change_dc_value(dc):
"""修改占空比"""
global dc_value
dc_value = dc
p.ChangeDutyCycle(dc_value)                      # 修改占空比
if __name__ == '__main__':
try:
set_pwm()                                      # 设置 PWM 信号
while True:
input_value = raw_input("请输入新的占空比（0~100）:")    # 输入占空比
new_dc = float(input_value) / 100                # 计算实际占空比
change_dc_value(new_dc)                            # 修改占空比
time.sleep(1)                                     # 睡眠指定时间
except KeyboardInterrupt:
pass                                             # 中断程序执行
finally:
GPIO.cleanup()                                    # 清除 GPIO 状态

Serial communication

#!/usr/bin/env python
import serial
import time
ser = serial.Serial('/dev/ttyS0', 9600, timeout=None)      # 初始化串口
while True:
send_data = input("请输入发送的数据:")         # 等待用户输入数据
ser.write(send_data.encode())                 # 发送数据
recv_data = ser.readline().decode().strip()    # 接收数据
print("接收到的数据：" + recv_data)             # 打印接收数据
time.sleep(0.1)                               # 等待一定时间
ser.close()                                           # 关闭串口

Create MQTT client

#!/usr/bin/env python
import paho.mqtt.client as mqtt
import time
host = "localhost"                                       # MQTT 服务器地址
port = 1883                                               # MQTT 服务端口
topic = "/test"                                          # MQTT 主题名
client_id = "client_" + str(int(time.time()))           # MQTT 客户端 ID
def on_connect(client, userdata, flags, rc):
print("Connected with result code " + str(rc))
client.subscribe(topic)                              # 订阅主题
def on_message(client, userdata, msg):
print("Received message '" + str(msg.payload) + "' on topic '"
+ msg.topic + "' with QoS " + str(msg.qos))
client = mqtt.Client(client_id)                          # 创建 MQTT 客户端对象
client.on_connect = on_connect                           # 设置连接回调函数
client.on_message = on_message                           # 设置接收消息回调函数
client.connect(host, port, keepalive=60)                  # 连接 MQTT 服务器
client.loop_forever()                                    # 持续运行客户端

Run Docker container

docker run busybox echo hello world

Train a neural network model

#!/usr/bin/env python
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
predictions = model(x_train[:1]).numpy()
predictions = tf.nn.softmax(predictions).numpy()
predicted_index = np.argmax(predictions)
print("预测结果：" + str(predicted_index) + ", 真实值：" + str(y_train[0]))
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print("
测试准确率:", test_acc)

Create API interface

from flask import Flask, request
app = Flask(__name__)
@app.route('/', methods=["GET"])
def index():
return {
    
    "success":True,"message":"Welcome to Smart Assistant!"}
@app.route('/api/v1/speak', methods=["POST"])
def speak():
text = request.form["text"]
# 执行语音合成逻辑
#...
response = {
    
    "success":True,"message":"Text has been spoken."}
return response
if __name__ == '__main__':
app.run(debug=True,port=5000)

5. Future development trends and challenges

More application scenarios
Currently, the main application scenarios of smart assistants are video playback, music playback, voice input, text input, schedule reminders, weather queries, garbage classification, information search, image recognition, stock quotes, etc. Although these application scenarios are rich, there are still needs for other application scenarios. For example, smart bracelets can provide heartbeat monitoring, respiration monitoring, blood oxygen detection and other functions; smart aircraft can provide wireless remote control, automatic driving and other functions; smart cars can provide tracking path planning, automatic cruise and other functions. In the future, smart assistants will also enter new fields, such as medical care and retail.
More intelligent
One of the future development directions of intelligent assistants is to become more intelligent. In the early days, smart assistants used hard-coded methods to implement specific functions. For example, a smart bracelet performs a certain function by pressing a button, which seems very old and outdated. Today, with the development of artificial intelligence, machine learning and other technologies, we are expected to make smart assistants have more intelligent functions. For example, smart bracelets can learn independently, handle complex tasks, and are used in smart sensors, health, smart houses, industrial automation and other fields.
More convenient application delivery
Another important development direction of smart assistants is more convenient application delivery. Now, many smart assistant applications need to rely on third-party platforms to function properly, which makes users very inconvenient. In order to allow users to obtain applications conveniently, we can build a self-service platform so that users can install smart assistant applications without purchasing, configuring servers, or installing software themselves. At the same time, we can also host the application on the cloud platform so that users can access, install, upgrade, and upgrade through the browser. In the future, we can further improve the services of intelligent assistants to make them smarter and more convenient.

6. Appendix Frequently Asked Questions and Answers

1. What smart assistant applications are there?

Smart assistant applications are an important research hotspot in the field of artificial intelligence. Here are some of the more popular smart assistant applications:

Chatbot: This is the simplest smart assistant application that interacts with users through text and voice. These chatbots have unique characteristics such as voice, focus, and response speed, and can provide users with fast and intelligent services.
Smart Voice Assistant: This type of smart assistant interacts with the user through voice. They have unique sounds, language skills, expression changes and other features, which can improve interaction efficiency and reduce the probability of errors.
Smart home assistant: This type of smart assistant achieves automated control by combining with smart sensors. They have complete rule engines, context understanding and other functions, and can automatically execute user instructions.
Smart photo album: This type of smart assistant organizes and retrieves the user's photos through technologies such as image recognition and semantic understanding. They help users discover content of interest, organize photos, and share photos.
Smart magazine: This type of smart assistant automatically generates article summaries and recommended information through technologies such as reading comprehension and knowledge graphs. They can help users read materials more efficiently and improve their reading experience.
Smart News: This type of smart assistant helps users browse, filter and retrieve information through a variety of technologies, such as natural language understanding, semantic understanding, automatic summarization, etc. They can implement information-based communication, content recommendation, search sorting and other functions.
Intelligent customer service: This type of intelligent assistant provides knowledge-based services through voice, image recognition and other technologies. They can realize customer service self-learning, online Q&A, one-click solutions and other functions.
Intelligent robot: This type of intelligent assistant implements a dialogue system through advanced machine learning algorithms. They can provide users with free, smart, and witty conversations and enable self-learning and self-improvement.

2. What hardware and software components are there?

Currently, the hardware and software components of smart assistant applications are as follows:

Hardware: The hardware of smart assistant applications mainly includes the following categories: smart sensors, smart controllers, displays, sound hardware, batteries, etc.
a. Smart sensor: Smart sensor is the core component of smart assistant applications. They can realize various sensing functions, such as sound and light recognition, environment sensing, image recognition, position sensing, touch sensing, etc.
b. Intelligent controller: The intelligent controller is responsible for realizing various control functions, such as motor control, steering gear control, button control, display screen display, speech synthesis, speech recognition, etc.
c. Display: The display can realize information presentation and is used to present the interface of the smart assistant application.
d. Sound hardware: Sound hardware can realize the sound output of smart assistant applications.
e. Battery: The battery can maintain the normal operation of the smart assistant application and meet different levels of usage requirements.
Software components: The software components of smart assistant applications include the following categories: operating systems, programming languages, AI algorithms, middleware, etc.
a. Operating system: The operating system can be used to implement operating system-level functions of smart assistant applications.
b. Programming language: Programming language can be used to implement the functions of the smart assistant application.
c. AI algorithm: AI algorithm can be used to implement intelligent algorithms for smart assistant applications.
d. Middleware: Middleware can be used to implement the communication mechanism of smart assistant applications.