统一消息系统

# 示例：使用Python实现一个简单的微信机器人

import requests

def send_message(text):

url = "https://api.example.com/send"

payload = {"text": text}

response = requests.post(url, json=payload)

return response.status_code

def receive_message():

url = "https://api.example.com/receive"

response = requests.get(url)

return response.json()

while True:

message = receive_message()

if message['text'] == '你好':

send_message('你好！有什么可以帮助你的吗？')

elif message['text'] == '联系方式':

send_message('我们的联系方式是：support@example.com')

# 示例：使用Python实现多渠道消息处理

def handle_message(channel, message):

if channel == 'wechat':

print("WeChat消息:", message)

send_message_to_wechat(message)

elif channel == 'sms':

print("SMS消息:", message)

send_sms(message)

elif channel == 'email':

print("Email消息:", message)

send_email(message)

# 模拟消息接收

messages = [

{'channel': 'wechat', 'content': '客户咨询产品价格'},

{'channel': 'sms', 'content': '订单已发货，请查收'},

{'channel': 'email', 'content': '您的账户已被登录'}

]

for msg in messages:

handle_message(msg['channel'], msg['content'])

# 示例：智能路由逻辑

def route_message(message):

if "紧急" in message:

assign_to_team("emergency")

elif "技术支持" in message:

assign_to_team("support")

else:

assign_to_team("general")

def assign_to_team(team_name):

print(f"消息已分配给 {team_name} 团队")

# 测试消息

messages = [

"紧急！服务器宕机了",

"如何重置密码？",

"客户反馈产品体验很好"

]

for msg in messages:

route_message(msg)

# 示例：自动化任务 - 生成报告

from fpdf import FPDF

def generate_report(data):

pdf = FPDF()

pdf.add_page()

pdf.set_font("Arial", size=12)

pdf.cell(200, 10, txt="客户报告", ln=1, align='C')

for key, value in data.items():

pdf.cell(200, 10, txt=f"{key}: {value}", ln=1)

pdf.output("report.pdf")

print("报告已生成")

# 模拟客户数据

customer_data = {

"姓名": "张三",

"订单号": "123456",

"金额": "500元",

"日期": "2025-04-05"

}

generate_report(customer_data)

# 示例：数据分析与可视化

import matplotlib.pyplot as plt

def analyze_data(data):

labels = list(data.keys())

values = list(data.values())

plt.bar(labels, values)

plt.xlabel('类别')

plt.ylabel('数量')

plt.title('消息类型统计')

plt.show()

# 模拟数据

message_types = {

"技术支持": 15,

"销售咨询": 8,

"投诉": 3,

"其他": 4

}

analyze_data(message_types)